Purpose

WORDS
### Sources & Resources Sources and resources are linked where applicable ## Setting Up ### Setting Up Environment

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# directory for input files
indir <- "../R_outputs/QuantPrep_Filter"

# make a directory for output files
if (! dir.exists("../R_outputs/DGE_Analyses")) {
 dir.create("../R_outputs/DGE_Analyses")
}
outdir <- "../R_outputs/DGE_Analyses"

Setting up Input Files

words

I can individual load the data for each pipeline separately, and add them to a list

# reading in count data
hf_htsh <- read.csv(file.path(indir, "hard_filtered_htsh.csv"), header = T, row.names = 1 ) #Hard-filtered (see Purpose) count matrices
hf_htss <- read.csv(file.path(indir,"hard_filtered_htss.csv"), header = T, row.names = 1 )
hf_kall <- read.csv(file.path(indir,"hard_filtered_kallisto.csv"), header = T, row.names = 1 )
hf_salm <- read.csv(file.path(indir,"hard_filtered_salmon.csv"), header = T, row.names = 1 )
hf_strh <- read.csv(file.path(indir,"hard_filtered_strgtieh.csv"), header = T, row.names = 1 )
hf_strs <- read.csv(file.path(indir,"hard_filtered_strgties.csv"), header = T, row.names = 1)

sf_htsh <- read.csv(file.path(indir,"soft_filtered_htsh.csv"), header = T, row.names = 1)  #Soft-filtered count matrices
sf_htss <- read.csv(file.path(indir,"soft_filtered_htss.csv"), header = T, row.names = 1)
sf_kall <- read.csv(file.path(indir,"soft_filtered_kallisto.csv"), header = T, row.names = 1)
sf_salm <- read.csv(file.path(indir,"soft_filtered_salmon.csv"), header = T, row.names = 1)
sf_strh <- read.csv(file.path(indir,"soft_filtered_strgtieh.csv"), header = T, row.names = 1)
sf_strs <- read.csv(file.path(indir,"soft_filtered_strgties.csv"), header = T, row.names = 1)

# make list of dataframes
datlist <- list(hf_htsh=hf_htsh,hf_htss=hf_htss,hf_kall=hf_kall,hf_salm=hf_salm,
                hf_strh=hf_strh,hf_strs=hf_strs,sf_htsh=sf_htsh,sf_htss=sf_htss,
                sf_kall=sf_kall,sf_salm=sf_salm,sf_strh=sf_strh,sf_strs=sf_strs)

# adding in sample metadata
samples <- read.table("../R_inputs/samples.txt", header = T) #Read in sample table
  #make new column for treatment (control vs Experiment)
samples <- samples %>% dplyr::select(SRRID, SAMPNAME) %>%  #select the sample ID and name
  mutate(Treat = ifelse(grepl("C", samples$SAMPNAME), "Restricted", #make new column for treatment (restricted vs adlib)
                            ifelse(grepl("E", samples$SAMPNAME), "AdLib", "Error")))
## make a note to change 
head(samples)

Or I can generate a List object with the file names and data

# sample metadata
samples <- read.table("../R_inputs/samples.txt", header = T) #Read in sample table
  #make new column for treatment (control vs Experiment)
samples <- samples %>% dplyr::select(SRRID, SAMPNAME) %>%  #select the sample ID and name
  mutate(Treat = ifelse(grepl("C", samples$SAMPNAME), "Restricted", #make new column for treatment (restricted vs adlib)
                            ifelse(grepl("E", samples$SAMPNAME), "AdLib", "Error")))

# generate data vector
files <- list() # empty list for file paths
count_data <- vector(mode = "list", length = 2) # empty list for data vector
files <- list.files(indir, ".csv", full.names = T) # populate list from input directory

count_data <- list(f_name = c(file_path_sans_ext(basename(files))), f_content = files %>% map(read.csv, header = T, row.names =1)) # populate list with file names, paths, and content
names(count_data$f_content) <- count_data$f_name # name matrices based on file names

DGE Functions

Library Visualization Function

words https://multithreaded.stitchfix.com/blog/2015/10/15/multiple-hypothesis-testing/ # explains MHT well, and in depth. pull what is needed, link the rest

run.Vis <- function(x, y) {
  y <- y[[1]][1]
  cat("Currently visualizing libraries for pipeline:", y, "\n\n") #print which file is being processed
  colnames(x) <- c(samples$SAMPNAME) #add column names
  cat("\nColumn names:", names(x), "\n\n") #print column names
  # possible issue, this object may not be the same across programs, but that shouldn't be a problem if it's just a data format. all information going into downstream programs are the same.
  dat <- DGEList(x, group = as.factor(c(samples$Treat))) #create DGEList object, merging counts, metadata, and specifies the grouping variable for samples 
  print(dat)
  
  
  # look at raw lib size in parallel
  barplot(dat$samples$lib.size*1e-6, names = 1:10, ylab = "Library size (millions)", xlab = y) #Make barplot of the library sizes
  # Saved in the object
  b.plot = recordPlot()
  dev.off()
  
  cpm <- cpm(dat)
  lcpm <- cpm(dat, log = TRUE)
  L <- mean(dat$samples$lib.size) * 1e-6
  M <- median(dat$samples$lib.size) * 1e-6
  cat("\n", "Mean and Median Library Size in Millions:", c(L, M),"\n")
  cnttab <- table(rowSums(dat$counts == 0) == 10) 
  cnttab 
 
  # MDS Plots
  col.group <- as.factor(c(samples$Treat))
  levels(col.group) <- brewer.pal(nlevels(col.group), "Set1")
  col.group <- as.character(col.group)
  mds.plot <- plotMDS(lcpm, labels = samples$SAMPNAME, col = col.group, main = paste0("Pipeline: ", y))
  # Saved in the object
  mds.plot = recordPlot()
  dev.off()
  # Density Plots to observe filtering effects (may need to reformat output to a list and then organize them after running the function to compare hard/soft filters)
  col.group <- brewer.pal(ncol(x), "Set3")
  lcpm.cutoff <- log2(10/M + 2/L)
  plot(density(lcpm[,1]), col = col.group[1], lwd = 2, ylim = c(0,0.26), las = 2, main = y)
  for (i in 2:ncol(x)){
  den <- density(lcpm[,i])
  lines(den$x, den$y, col = col.group[i], lwd = 2)
  }
  legend("topright", samples$SAMPNAME, text.col = col.group, bty = "n")
  abline(v = lcpm.cutoff, lty = 3)
  # Saved in the object
  density.res = recordPlot()
  dev.off()
 
  # Box Plots
  dat2 <- calcNormFactors(dat, method = "TMM")
  lcpm2 <- cpm(dat2, log=TRUE)
  print(head(lcpm))
  print(head(lcpm2))
  dat$samples$norm.factors
  dat2$samples$norm.factors
  par(mfrow=c(1,2)) #getting 
  boxplot(lcpm, las = 2, col = col.group, main = "Unnormalized data", ylab = "Log-CPM")
  boxplot(lcpm2, las = 2, col = col.group, main = "Normalized data", ylab = "Log-CPM")
  box.res = recordPlot()
  dev.off()
  
  pdf(file = paste0(outdir,"/",y,"_dataExploration.pdf"))
  print(b.plot)
  print(mds.plot)
  print(density.res)
  print(box.res)
  dev.off()
}

DESeq2 DGE Function

words

run.DESeq <- function(x, y) {
  y <- y[[1]][1]
  cat("Currently proccessing:", y, "with DESeq \n") #print which file is being processed
  colnames(x) <- c(samples$SAMPNAME) #add column names
  cat("\nColumn names:", names(x), "\n\n") #print column names 
  
  dat <- DESeqDataSetFromMatrix(countData = x, colData = samples, 
                                design = ~Treat) #create DESeq object, merging counts, metadata, and specifies the predictor variable for gene counts
  print(dat)
  cat("\nResults Below: \n")
  mod <- DESeq(dat, minReplicatesForReplace = Inf) #running the DESeq function. minReplicatesForReplace=Inf prevents replacement of outlier counts
  res <- results(mod, independentFiltering = FALSE,cooksCutoff = FALSE, contrast = c("Treat", "Restricted", "AdLib"),
                 pAdjustMethod = "fdr") #store results table. skipping outlier adjustments and additional low count filtering. using a false discovery rate p-value adjustment
  print(head(res))
  print(summary(res))
  
  # make data frame output, reorder, and filter
  reslist <- list( GeneID = res@rownames, meanExpr = res@listData$baseMean, logFC = res@listData$log2FoldChange, 
                   pval = res@listData$pvalue, adj.pval = res@listData$padj)
  resdf <- as.data.frame(do.call(cbind, reslist)) %>% mutate(meanExpr = as.numeric(meanExpr), pval = as.numeric(pval), 
                                                             adj.pval = as.numeric(adj.pval))
  
  resOrdered <- resdf[order(as.numeric(resdf$adj.pval)),] #results reordered by the adjusted pvalue
  resSig <- subset(resOrdered, as.numeric(adj.pval) < 0.05)
  print(head (resSig))
  print(summary(resSig))
  
  out <- resSig
  
  write.csv(as.data.frame(out),file=paste0(outdir,"/",y,"_DESeq2.csv")) #write results to a new csv
  
  pdf(file = paste0(outdir,"/",y,"_DESeq.pdf"))
  DESeq2::plotMA(res)
  dev.off()
  
}

edgeR DGE Function

words

run.EdgeR <- function(x, y) {
  y <- y[[1]][1]
  cat("Currently proccessing:", y, "with edgeR \n") #print which file is being processed
  colnames(x) <- c(samples$SAMPNAME) #add column names
  cat("\nColumn names:", names(x), "\n\n") #print column names 
  
  
  dat <- DGEList(x, group = samples$Treat) #create DGEList object, merging counts, metadata, and specifies the grouping variable for samples 
  
  # est common & tagwise dispersion
  mod <- estimateCommonDisp(dat)
  mod <- estimateTagwiseDisp(mod)
  
  # perform exact test btwn caloric restriction & ad lib groups, store as 'res'
  modTest <- exactTest(mod)
  res <- topTags(modTest, n = nrow(modTest$table))
  
  # extract significant differentially expressed genes, sort, & write to csv
  resOrdered <- res$table[order(res$table$logFC),]
  resSig <- resOrdered[resOrdered$FDR<0.05,]
  print(head(resOrdered))
  
  out <- resSig %>% dplyr::select(logFC, logCPM, PValue, FDR) %>% dplyr::rename(meanExpr = logCPM, pval = PValue, adj.pval = FDR)
  write.csv(as.data.frame(out),file = paste0(outdir,"/",y,"_edgeR.csv")) #write results to a new csv

  cat("The number of significant DE genes is: ", nrow(resSig),"\n\n")
  
  pdf(file = paste0(outdir,"/",y,"_edgeR.pdf"))
  edgeR::plotMD.DGEExact(modTest)
  #plotMD(res, column = 5, main = paste(colnames(res)[1],y,sep = "_"), xlim = c(-0.1,20))
  dev.off()
  
  
}

LimmaVoom DGE Function

words Summarize & simplify: “What is voom doing? Counts are transformed to log2 counts per million reads (CPM), where “per million reads” is defined based on the normalization factors we calculated earlier A linear model is fitted to the log2 CPM for each gene, and the residuals are calculated A smoothed curve is fitted to the sqrt(residual standard deviation) by average expression (see red line in plot above) The smoothed curve is used to obtain weights for each gene and sample that are passed into limma along with the log2 CPMs. More details at https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29

run.LimVoo <- function(x,y) {
  y <- y[[1]][1]
  cat("Currently proccessing:", y ,"with Limma-Voom \n") #print which file is being processed by which function as a sanity check
  colnames(x) <- c(samples$SAMPNAME) #add column names to the data object
  cat("\nColumn names:", names(x), "\n\n") #print column names as a sanity check for order
  
  Treat <- c(samples$Treat)
  group=Treat
  dat <- DGEList(x, group = Treat) #create DGEList object, merging counts, metadata, and specifies the grouping variable for samples 
  #print(class(dat))
  
  # Normalization (based on the plots, is this necessary?)
  dat <- calcNormFactors(dat, method = "TMM")
  dat$samples$norm.factors
  dat
  
  mod <- model.matrix(~0 + group)
  mod
  
  varMod <- voom(dat, mod, plot = T) # Would be nice to stop and compare hard/soft filtering again here
  
  modFit <- lmFit(varMod, mod)
  #print(head(coef(modFit)))
  
  contr <- makeContrasts(groupAdLib - groupRestricted, levels = colnames(coef(modFit)))
  #print(head(contr))
  
  fitContr <- contrasts.fit(modFit, contr)
  fitContr <- eBayes(fitContr)
  
  res <- topTable(fitContr, sort.by = "P", n = Inf)
  print(head(res, 8)) 
  cat("Results where FDR is less than 0.01: ", length(which(res$adj.P.Val < 0.01)), "\n")
  cat("Results where FDR is less than 0.05: ", length(which(res$adj.P.Val < 0.05)), "\n")
  cat("Results where FDR is less than 0.1: ", length(which(res$adj.P.Val < 0.1)), "\n")
  
  out <- res %>% dplyr::select(logFC, AveExpr, P.Value, adj.P.Val) %>% dplyr::rename(meanExpr = AveExpr, pval = P.Value, adj.pval = adj.P.Val)
  print(head(out))
  
  etRes <- decideTests(fitContr)
  print(summary(etRes))
  
  write.csv(as.data.frame(out),file = paste0(outdir,"/",y,"_LimmaVoom.csv")) #write results to a new csv
  
  pdf(file = paste0(outdir,"/",y,"_LimmaVoom.pdf"))
  plotMD(fitContr, column = 1, status = etRes[,1], main = paste(colnames(fitContr)[1],y,sep = "_"), xlim = c(-0.1,20))
  varMod <- voom(dat, mod, plot = T)
  dev.off()
  
}

Ballgown DGE

words Summarize & simplify: There are many ballgown specific input files that make it difficult to use the previously filtered data with these programs. Only things processed with stringtie with the for ballgown output are readily formatted for this program. These will not be run within a function.? Some resources: https://rnabio.org/module-03-expression/0003/04/01/DE_Visualization/ https://rstudio-pubs-static.s3.amazonaws.com/289617_cb95459057764fdfb4c42b53c69c6d3f.html https://davetang.org/muse/2017/10/25/getting-started-hisat-stringtie-ballgown/


# We loaded our "phenotype" data in the beginning, so we don't need to repeat this step. 
# create a ballgown object for the star and hisat2 outputs; stringtie and ballgown are complementary programs

bg_star <- ballgown(dataDir = "../R_inputs/ballgown_star/", samplePattern = "SRR", pData = samples)
bg_hisat <- ballgown(dataDir = "../R_inputs/ballgown_hisat/", samplePattern = "SRR", pData = samples)

# check out the objects
class(bg_star)
class(bg_hisat)

bg_star
bg_hisat

# filtering, following previous logic for pipeline specific

bg_star_f1 <- ballgown::subset(bg_star, 
                               "rowSums(gexpr(bg_star)==0) <= 5", 
                               genomesubset=TRUE) # first filter, remove rows with 6 or more 0s
bg_star_f1
bg_star_fltrd <- ballgown::subset(bg_star_f1, 
                                  "rowSums(gexpr(bg_star_f1)) >= 21") # second filter, remove rows that sum to less than 21
bg_star_fltrd


bg_hisat_f1 <- ballgown::subset(bg_hisat, 
                                "rowSums(gexpr(bg_hisat)==0) <= 5", 
                                genomesubset=TRUE) # first filter, remove rows with 6 or more 0s
bg_hisat_f1
bg_hisat_fltrd <- ballgown::subset(bg_hisat_f1, 
                                   "rowSums(gexpr(bg_hisat_f1)) >= 21") # second filter, remove rows that sum to less than 21
bg_hisat_fltrd

# run dge analysis and output data to file (should filter by qvalue? -- what did I filter by with other tables?)
bg_star_genes <- stattest(bg_star_fltrd,
                          feature="gene",
                          covariate="Treat",
                          getFC=TRUE, meas="FPKM")
dim(bg_star_genes)
table(bg_star_genes$qval<0.05)


bg_hisat_genes <- stattest(bg_hisat_fltrd,
                          feature="gene",
                          covariate="Treat",
                          getFC=TRUE, meas="FPKM")

dim(bg_hisat_genes)
table(bg_hisat_genes$qval<0.05)

# output results
# extract significant differentially expressed genes, sort, & write to csv

bg_hisat_genes[,"de"] <- log2(bg_hisat_genes[,"fc"])
sigpi = which(bg_hisat_genes[,"pval"]<0.05)
sigp = bg_hisat_genes[sigpi,]
sigde = which(abs(sigp[,"de"]) >= 2)
sig_tn_de = sigp[sigde,]
o = order(sig_tn_de[,"qval"], -abs(sig_tn_de[,"de"]), decreasing=FALSE)
output = sig_tn_de[o,c("id","fc","pval","qval","de")]
write.csv(as.data.frame(output),file = paste0(outdir,"/","hisat_Ballgown.csv")) #write results to a new csv

bg_star_genes[,"de"] <- log2(bg_star_genes[,"fc"])
sigpi = which(bg_hisat_genes[,"pval"]<0.05)
sigp = bg_hisat_genes[sigpi,]
sigde = which(abs(sigp[,"de"]) >= 2)
sig_tn_de = sigp[sigde,]
o = order(sig_tn_de[,"qval"], -abs(sig_tn_de[,"de"]), decreasing=FALSE)
output <- sig_tn_de[o,c("id","fc","pval","qval","de")]
write.csv(as.data.frame(output),file = paste0(outdir,"/","star_Ballgown.csv")) #write results to a new csv

# visualize results

bg_star_genes$mean <- rowMeans(texpr(bg_star_fltrd))
bg_star_plot <- ggplot(bg_star_genes, aes(log2(mean), log2(fc), colour = qval<0.05)) +
  scale_color_manual(values=c("#999999", "#FF0000")) +
  geom_point() +
  geom_hline(yintercept=0)

bg_hisat_genes$mean <- rowMeans(texpr(bg_hisat_fltrd))
bg_hisat_plot <- ggplot(bg_hisat_genes, aes(log2(mean), log2(fc), colour = qval<0.05)) +
  scale_color_manual(values=c("#999999", "#FF0000")) +
  geom_point() +
  geom_hline(yintercept=0)

bg_star_plot
bg_hisat_plot

Apply DGE functions

I am applying each function in loop here


cnt <- 1
for (i in datlist){
run.Vis(i, names(datlist)[cnt])
cnt <- cnt +1
}

cnt <- 1
for (i in datlist){
run.DESeq(i, names(datlist)[cnt])
cnt <- cnt +1
}

cnt <- 1
for (i in datlist){
run.EdgeR(i, names(datlist)[cnt])
cnt <- cnt +1
}

cnt <- 1
for (i in datlist){
run.LimVoo(i, names(datlist)[cnt])
cnt <- cnt +1
}

I can also map or mapply over the data set, looping over each DEseq function

# List of functions needed to run on count matrices
funct <- c( "run.Vis","run.DESeq", "run.EdgeR", "run.LimVoo")

# Apply each function to each count data set in List object
for (func in funct) {
mapply(func, count_data$f_content, count_data$f_name)
}
Currently visualizing libraries for pipeline: hard_filtered_htsh 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000004  269  325  338  388  341  271  351  249  264  441
FUN_000005 1114 1569 1092 1321 2181 2014 2129 2293 2210 2940
FUN_000006  194  273  224  251  468  361  332  474  424  579
FUN_000007 2240 3254 1947 2347 2492 1713 2822 2181 2464 3268
FUN_000008   99  364  241  222  179  150  107  223  229  231
14296 more rows ...

$samples


 Mean and Median Library Size in Millions: 21.78049 20.67801 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4        C5         C2        C3         E2        E3         C6        E1        E4         E6
FUN_000004  4.176416  4.048445  4.3674991  4.647334  3.5752138  3.659400  4.2648155  3.175810  3.611003  3.7621706
FUN_000005  6.220919  6.313417  6.0549387  6.411098  6.2429526  6.543996  6.8596819  6.365701  6.666861  6.4908132
FUN_000006  3.707696  3.798431  3.7772371  4.021848  4.0289313  4.070490  4.1849221  4.097576  4.290432  4.1526264
FUN_000007  7.227775  7.364923  6.8883434  7.239605  6.4350486  6.310707  7.2659438  6.293537  6.823682  6.6432572
FUN_000008  2.746837  4.211086  3.8820898  3.845785  2.6554257  2.814502  2.5665850  3.018416  3.407469  2.8381336
FUN_000009 -1.773739 -1.009086 -0.9506003 -1.811569 -0.9769934 -0.112509 -0.9217186 -1.881279 -1.122484 -0.3881246
                  C4        C5         C2        C3         E2         E3         C6        E1        E4         E6
FUN_000004  4.288257  4.050506  4.4003803  4.594051  3.5792943  3.6276310  4.2777725  3.214995  3.527944  3.7271295
FUN_000005  6.333191  6.315506  6.0879330  6.357686  6.2470880  6.5120522  6.8727077  6.405278  6.583263  6.4555925
FUN_000006  3.819318  3.800484  3.8100353  3.968664  4.0330295  4.0386704  4.1978743  4.136968  4.207143  4.1175351
FUN_000007  7.340117  7.367016  6.9213601  7.186169  6.4391853  6.2787680  7.2789730  6.333111  6.740077  6.6080333
FUN_000008  2.857712  4.213150  3.9149053  3.792638  2.6594481  2.7828948  2.5793567  3.057550  3.324503  2.8032822
FUN_000009 -1.696979 -1.008177 -0.9241194 -1.849017 -0.9743778 -0.1417137 -0.9116873 -1.856377 -1.189943 -0.4196272

Currently visualizing libraries for pipeline: hard_filtered_htss 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000004  256  316  329  360  344  270  330  239  270  418
FUN_000005 1122 1597 1124 1299 2272 2140 2197 2417 2220 2928
FUN_000006  174  240  203  218  433  351  323  426  388  501
FUN_000007 2180 3166 1952 2262 2491 1795 2900 2149 2446 3121
FUN_000008  120  400  249  259  209  182  122  249  268  242
14296 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 23.2617 21.99226 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                 C4       C5       C2       C3       E2       E3       C6       E1       E4       E6
FUN_000004 4.019083 3.950052 4.248080 4.487181 3.484842 3.508325 4.047829 3.004274 3.537596 3.620980
FUN_000005 6.145023 6.280968 6.015938 6.334510 6.198892 6.485347 6.776438 6.328410 6.567711 6.420653
FUN_000006 3.465628 3.555701 3.555518 3.767110 3.814522 3.884319 4.017059 3.831314 4.057441 3.880617
FUN_000007 7.102428 7.267474 6.811436 7.134053 6.331506 6.231986 7.176682 6.159051 6.707455 6.512656
FUN_000008 2.934619 4.288441 3.848234 4.014291 2.773076 2.944565 2.624974 3.062788 3.526949 2.839798
FUN_000009 1.273124 1.741883 2.117911 1.929611 2.109904 2.215705 1.838380 2.127757 2.527024 1.857021
                 C4       C5       C2       C3       E2       E3       C6       E1       E4       E6
FUN_000004 4.122509 3.942661 4.278011 4.414241 3.489479 3.485646 4.062345 3.052664 3.461097 3.599394
FUN_000005 6.248867 6.273554 6.045971 6.261368 6.203573 6.462531 6.791028 6.377286 6.490713 6.398949
FUN_000006 3.568799 3.548319 3.585360 3.694350 3.819169 3.861604 4.031574 3.879940 3.980770 3.859008
FUN_000007 7.206332 7.260057 6.841487 7.060878 6.336188 6.209174 7.191275 6.207919 6.630451 6.490951
FUN_000008 3.037435 4.281044 3.878119 3.941459 2.777678 2.921961 2.639344 3.111199 3.450455 2.818311
FUN_000009 1.373445 1.734596 2.147353 1.858032 2.114456 2.193253 1.852581 2.175696 2.451102 1.835763

Currently visualizing libraries for pipeline: hard_filtered_kallisto 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000004  149  175  181  208  190  141  189  136  148  240
FUN_000005  637  920  634  766 1255 1151 1212 1312 1237 1681
FUN_000006  107  153  120  141  259  190  174  250  223  319
FUN_000007 1271 1883 1197 1335 1403  993 1593 1247 1406 1846
FUN_000008   70  230  143  149  112   95   70  134  137  143
14296 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 13.64146 12.93434 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                 C4       C5       C2       C3       E2       E3       C6       E1       E4       E6
FUN_000004 4.000170 3.820597 4.152520 4.420829 3.425608 3.420350 4.052482 2.991095 3.452382 3.563222
FUN_000005 6.085989 6.202702 5.952490 6.294379 6.132422 6.432029 6.722613 6.237140 6.498446 6.356014
FUN_000006 3.527648 3.628924 3.565598 3.864626 3.867307 3.845550 3.934281 3.857234 4.037315 3.969306
FUN_000007 7.081034 7.234559 6.867751 7.094653 6.292931 6.219397 7.116491 6.163979 6.682916 6.490866
FUN_000008 2.925126 4.211291 3.815703 3.943463 2.676751 2.860189 2.641032 2.970119 3.342511 2.828290
FUN_000009 1.044054 1.253268 2.015929 1.488350 1.804675 1.844596 1.268461 1.354294 2.106342 1.366631
                 C4       C5       C2       C3       E2       E3       C6       E1       E4       E6
FUN_000004 4.093236 3.814769 4.165635 4.340900 3.435392 3.400927 4.063512 3.048664 3.387398 3.548506
FUN_000005 6.179696 6.196829 5.965686 6.214041 6.142327 6.412376 6.733731 6.295666 6.432676 6.341146
FUN_000006 3.620388 3.623104 3.578656 3.784961 3.877128 3.826060 3.945303 3.915286 3.972033 3.954546
FUN_000007 7.174839 7.228682 6.880962 7.014250 6.302839 6.199748 7.127612 6.222500 6.617133 6.475995
FUN_000008 3.017263 4.205450 3.828788 3.863754 2.686438 2.840890 2.651891 3.027672 3.277598 2.813692
FUN_000009 1.131439 1.247710 2.028659 1.412144 1.814162 1.825693 1.278882 1.409601 2.042736 1.352552

Currently visualizing libraries for pipeline: hard_filtered_salmon 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2  E3   C6   E1   E4   E6
FUN_000004  127  153  158  193  168 134  170  121  126  199
FUN_000005  495  730  487  606  980 884  945 1026  980 1322
FUN_000006   71  116   87   97  178 133  128  192  170  230
FUN_000007 1215 1786 1108 1265 1335 944 1545 1192 1360 1775
FUN_000008   54  208  129  132  100  83   67  123  110  134
14296 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 11.82035 11.17817 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                 C4       C5       C2       C3       E2       E3       C6       E1       E4       E6
FUN_000004 3.981622 3.858952 4.209716 4.545035 3.453274 3.540352 4.092312 3.019626 3.421466 3.493106
FUN_000005 5.932688 6.099958 5.824774 6.188608 5.979009 6.244254 6.555307 6.076779 6.360832 6.206453
FUN_000006 3.154820 3.464902 3.359605 3.562803 3.535437 3.529703 3.687609 3.674551 3.847668 3.699046
FUN_000007 7.225774 7.388616 7.008322 7.248611 6.423966 6.338790 7.263519 6.292629 6.832755 6.630706
FUN_000008 2.768563 4.297549 3.920126 4.001796 2.719888 2.862137 2.770844 3.042787 3.228856 2.933048
FUN_000009 1.584730 1.746739 2.157899 2.205458 2.201107 2.285448 1.826521 1.942639 2.650457 1.718160
                 C4       C5       C2       C3       E2       E3       C6       E1       E4       E6
FUN_000004 4.092552 3.840990 4.227488 4.441714 3.468429 3.514468 4.088733 3.080457 3.359803 3.500455
FUN_000005 6.044467 6.081818 5.842648 6.084750 5.994347 6.218031 6.551690 6.138709 6.298275 6.213884
FUN_000006 3.264864 3.447010 3.377253 3.460252 3.550605 3.503822 3.684045 3.735838 3.785742 3.706408
FUN_000007 7.337728 7.370448 7.026225 7.144622 6.439314 6.312563 7.259899 6.354579 6.770160 6.638140
FUN_000008 2.877982 4.279527 3.937863 3.898837 2.734897 2.836493 2.767334 3.103639 3.167340 2.940351
FUN_000009 1.690763 1.729526 2.175187 2.105342 2.215958 2.260120 1.823117 2.002085 2.589520 1.725275

Currently visualizing libraries for pipeline: hard_filtered_strgtieh 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000004  250  284  299  361  322  261  324  231  237  374
FUN_000005 1036 1425 1017 1209 2053 1950 2023 2160 2042 2645
FUN_000006  170  238  192  213  411  329  302  418  374  483
FUN_000007 2082 2989 1824 2164 2395 1729 2716 2085 2339 2981
FUN_000008  103  331  221  226  177  153  110  220  227  225
14296 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 20.80694 19.70391 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                   C4         C5        C2         C3           E2        E3         C6          E1         E4
FUN_000004  4.1447179  3.9602825 4.3093162  4.6478301  3.546906331 3.6060539  4.1705685  3.11996771 3.50532705
FUN_000005  6.1897815  6.2801304 6.0704718  6.3876884  6.209468390 6.4975083  6.8065172  6.33071819 6.60151570
FUN_000006  3.5920092  3.7070740 3.6741698  3.8905233  3.896412419 3.9377359  4.0696841  3.96842405 4.15899792
FUN_000007  7.1957729  7.3478974 6.9123446  7.2268436  6.431492797 6.3241682  7.2311882  6.27979624 6.79724335
FUN_000008  2.8765800  4.1799572 3.8756837  3.9754546  2.693282396 2.8435524  2.6269823  3.05037572 3.44367016
FUN_000009 -0.2006232 -0.1310481 0.5016592 -0.1105362 -0.005665078 0.5701681 -0.3968291 -0.07072992 0.06527644
                  E6
FUN_000004 3.6274368
FUN_000005 6.4399278
FUN_000006 3.9938873
FUN_000007 6.6122764
FUN_000008 2.9017352
FUN_000009 0.1106819
                   C4         C5        C2        C3        E2        E3         C6          E1            E4
FUN_000004  4.2372617  3.9543807 4.3155487  4.568926 3.5642762 3.5910525  4.1872014  3.16642553  3.433874e+00
FUN_000005  6.2826968  6.2741997 6.0767260  6.308566 6.2269595 6.4824037  6.8232250  6.37763273  6.529511e+00
FUN_000006  3.6843242  3.7011792 3.6803853  3.811834 3.9138133 3.9227100  4.0863106  4.01510954  4.087317e+00
FUN_000007  7.2887477  7.3419629 6.9186028  7.147680 6.4489871 6.3090656  7.2478997  6.32670879  6.725229e+00
FUN_000008  2.9684331  4.1740503 3.8819054  3.896736 2.7105361 2.8286340  2.6434443  3.09680824  3.372244e+00
FUN_000009 -0.1175855 -0.1363694 0.5074921 -0.181336 0.0101593 0.5560253 -0.3822238 -0.02844179 -3.606415e-05
                  E6
FUN_000004 3.6193492
FUN_000005 6.4317862
FUN_000006 3.9857855
FUN_000007 6.6041338
FUN_000008 2.8936887
FUN_000009 0.1032508

Currently visualizing libraries for pipeline: hard_filtered_strgties 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000004  241  273  287  341  318  258  307  221  231  356
FUN_000005 1021 1409 1010 1166 2079 1994 2024 2187 2023 2599
FUN_000006  154  215  181  189  385  322  294  386  351  437
FUN_000007 2013 2923 1801 2101 2373 1753 2753 2046 2299 2887
FUN_000008  116  353  224  249  195  166  120  235  246  222
14296 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 21.88414 20.82459 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                 C4       C5       C2       C3       E2       E3       C6       E1       E4       E6
FUN_000004 4.031486 3.857754 4.187968 4.525181 3.451661 3.481319 3.993907 2.971615 3.387418 3.504636
FUN_000005 6.108191 6.218102 5.998013 6.294848 6.150208 6.421227 6.707769 6.263260 6.506745 6.362583
FUN_000006 3.389931 3.515641 3.527137 3.678393 3.725394 3.798660 3.931851 3.768966 3.986682 3.798237
FUN_000007 7.086613 7.269962 6.831550 7.143609 6.340800 6.235599 7.151230 6.167231 6.691081 6.514038
FUN_000008 2.985240 4.226459 3.832449 4.073669 2.753690 2.851660 2.651542 3.059228 3.477415 2.830313
FUN_000009 1.548618 1.772277 2.194649 2.113882 2.041703 2.228124 1.908808 2.239395 2.545772 2.038383
                 C4       C5       C2       C3       E2       E3       C6       E1       E4       E6
FUN_000004 4.117477 3.846199 4.194365 4.435483 3.464469 3.470304 4.017632 3.022739 3.322924 3.501217
FUN_000005 6.194537 6.206483 6.004430 6.204887 6.163101 6.410127 6.731604 6.314907 6.441730 6.359135
FUN_000006 3.475662 3.504107 3.533518 3.588992 3.738219 3.787626 3.955569 3.820338 3.921988 3.794812
FUN_000007 7.173013 7.258336 6.837970 7.053599 6.353696 6.224501 7.175071 6.218874 6.626058 6.510589
FUN_000008 3.070736 4.214886 3.838838 3.984108 2.766435 2.840698 2.675064 3.110386 3.412885 2.826915
FUN_000009 1.632474 1.760977 2.200962 2.025790 2.054344 2.217245 1.932106 2.290133 2.481744 2.035024

Currently visualizing libraries for pipeline: pipeline_filtered_htsh 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000001    0   11    8   10   12    7    7    4    5   19
FUN_000002    1    9    8   10    1    2    7    0    0    1
FUN_000003    0    4    4    8    3    5    1    2    0    1
FUN_000004  269  325  338  388  341  271  351  249  264  441
FUN_000005 1114 1569 1092 1321 2181 2014 2129 2293 2210 2940
13973 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 21.79551 20.69244 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4         C5         C2         C3         E2        E3        C6        E1        E4         E6
FUN_000001 -3.445959 -0.6252614 -0.7913647 -0.4446415 -0.9779168 -1.266950 -1.080076 -2.087022 -1.638125 -0.5735183
FUN_000002 -2.656539 -0.8700888 -0.7913647 -0.4446415 -2.9836518 -2.440113 -1.080076 -3.445959 -3.445959 -3.0315087
FUN_000003 -3.445959 -1.7654237 -1.5787164 -0.7222154 -2.3529189 -1.630259 -2.773737 -2.612072 -3.445959 -3.0315087
FUN_000004  4.175609  4.0474510  4.3664448  4.6461829  3.5743054  3.658382  4.263660  3.174905  3.609989  3.7611613
FUN_000005  6.220113  6.3124228  6.0538841  6.4099459  6.2420448  6.542979  6.858526  6.364797  6.665846  6.4898038
FUN_000006  3.706889  3.7974364  3.7761829  4.0206963  4.0280231  4.069472  4.183767  4.096671  4.289418  4.1516171
                  C4         C5         C2         C3         E2        E3        C6        E1        E4         E6
FUN_000001 -3.450021 -0.6235803 -0.7681701 -0.4941732 -0.9763768 -1.292662 -1.067003 -2.062228 -1.698667 -0.6029724
FUN_000002 -2.610754 -0.8685826 -0.7681701 -0.4941732 -2.9858363 -2.458086 -1.067003 -3.450021 -3.450021 -3.0428676
FUN_000003 -3.450021 -1.7648809 -1.5592268 -0.7703492 -2.3533458 -1.654202 -2.769855 -2.595315 -3.450021 -3.0428676
FUN_000004  4.286576  4.0500397  4.3945857  4.5902381  3.5770258  3.626679  4.280726  3.217368  3.526848  3.7278515
FUN_000005  6.331508  6.3150408  6.0821232  6.3538641  6.2448094  6.511099  6.875676  6.407679  6.582164  6.4563230
FUN_000006  3.817640  3.8000181  3.8042519  3.9648570  4.0307577  4.037718  4.200827  4.139356  4.206046  4.1182594

Currently visualizing libraries for pipeline: pipeline_filtered_htss 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000004  256  316  329  360  344  270  330  239  270  418
FUN_000005 1122 1597 1124 1299 2272 2140 2197 2417 2220 2928
FUN_000006  174  240  203  218  433  351  323  426  388  501
FUN_000007 2180 3166 1952 2262 2491 1795 2900 2149 2446 3121
FUN_000008  120  400  249  259  209  182  122  249  268  242
14326 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 23.28884 22.01846 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                 C4       C5       C2       C3       E2       E3       C6       E1       E4       E6
FUN_000004 4.017613 3.948342 4.246172 4.485213 3.483322 3.506750 4.045855 3.002785 3.535872 3.619274
FUN_000005 6.143553 6.279258 6.014030 6.332541 6.197373 6.483773 6.774464 6.326923 6.565987 6.418946
FUN_000006 3.464157 3.553992 3.553611 3.765143 3.813002 3.882744 4.015086 3.829826 4.055717 3.878910
FUN_000007 7.100958 7.265764 6.809527 7.132084 6.329986 6.230412 7.174707 6.157564 6.705731 6.510949
FUN_000008 2.933147 4.286731 3.846327 4.012323 2.771555 2.942989 2.623003 3.061299 3.525226 2.838092
FUN_000009 1.271647 1.740174 2.116006 1.927648 2.108382 2.214129 1.836412 2.126266 2.525301 1.855315
                 C4       C5       C2       C3       E2       E3       C6       E1       E4       E6
FUN_000004 4.148632 3.936795 4.300857 4.392875 3.481100 3.467173 4.055448 3.044699 3.467943 3.595457
FUN_000005 6.275102 6.267677 6.068899 6.239950 6.195161 6.443956 6.784116 6.369276 6.497629 6.395008
FUN_000006 3.594855 3.542458 3.608135 3.673033 3.810782 3.843104 4.024677 3.871953 3.987640 3.855070
FUN_000007 7.232583 7.254178 6.864429 7.039451 6.327775 6.190602 7.184362 6.199911 6.637368 6.487010
FUN_000008 3.063397 4.275175 3.900928 3.920122 2.769324 2.903545 2.632478 3.103232 3.457300 2.814376
FUN_000009 1.398743 1.728783 2.169810 1.837026 2.106139 2.174952 1.845751 2.167772 2.457867 1.831837

Currently visualizing libraries for pipeline: pipeline_filtered_kallisto 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
            C4  C5  C2  C3   E2   E3   C6   E1   E4   E6
FUN_000001  29  42  33  26   44   39   34   29   37   51
FUN_000002  26  36  33  30   10   12   23    5    0    4
FUN_000003  10  19  20   7    8   15    6   22   12   17
FUN_000004 149 175 181 208  190  141  189  136  148  240
FUN_000005 637 920 634 766 1255 1151 1212 1312 1237 1681
14429 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 13.68269 12.97388 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4        C5       C2         C3         E2         E3        C6         E1          E4
FUN_000001 1.6888730 1.8039906 1.744592  1.4832311  1.3750507 1.61252454  1.629515  0.8526073  1.50499267
FUN_000002 1.5388591 1.5916285 1.744592  1.6795896 -0.5095473 0.05946058  1.097847 -1.2098736 -2.77428056
FUN_000003 0.2720316 0.7309146 1.062462 -0.2184032 -0.7583105 0.34033730 -0.585559  0.4907451  0.02753811
FUN_000004 3.9963970 3.8162406 4.147647  4.4156737  3.4216075 3.41590275  4.047288  2.9871538  3.44816344
FUN_000005 6.0822201 6.1983457 5.947614  6.2892190  6.1284255 6.42758086  6.717414  6.2332051  6.49422934
FUN_000006 3.5238726 3.6245683 3.560728  3.8594728  3.8633077 3.84110249  3.929088  3.8532964  4.03309717
                    E6
FUN_000001  1.38938191
FUN_000002 -1.55567399
FUN_000003 -0.04295002
FUN_000004  3.55901974
FUN_000005  6.35181344
FUN_000006  3.96510398
                  C4        C5       C2         C3         E2         E3         C6         E1         E4
FUN_000001 1.7795347 1.8000840 1.755706  1.4026705  1.3826369 1.59140942  1.6458455  0.9087818  1.4419718
FUN_000002 1.6290604 1.5877447 1.755706  1.5984519 -0.5032787 0.04034782  1.1138053 -1.1693694 -2.7749077
FUN_000003 0.3556605 0.7271663 1.073253 -0.2887857 -0.7523821 0.32068746 -0.5722769  0.5455204 -0.0293176
FUN_000004 4.0904072 3.8122261 4.159193  4.3311675  3.4295643 3.39404836  4.0642982  3.0471117  3.3825785
FUN_000005 6.1768774 6.1943024 5.959232  6.2042797  6.1364825 6.40546634  6.7345553  6.2941595  6.4278515
FUN_000006 3.6175546 3.6205589 3.572223  3.7752467  3.8712957 3.81917231  3.9460851  3.9137572  3.9672116
                    E6
FUN_000001  1.37516295
FUN_000002 -1.56449532
FUN_000003 -0.05579868
FUN_000004  3.54417302
FUN_000005  6.33681316
FUN_000006  3.95021324

Currently visualizing libraries for pipeline: pipeline_filtered_salmon 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
            C4  C5  C2  C3  E2  E3  C6   E1  E4   E6
FUN_000001  28  40  30  24  40  38  32   27  36   48
FUN_000002  24  35  29  24   9  12  22    4   0    4
FUN_000004 127 153 158 193 168 134 170  121 126  199
FUN_000005 495 730 487 606 980 884 945 1026 980 1322
FUN_000006  71 116  87  97 178 133 128  192 170  230
13663 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 11.83682 11.19426 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                 C4       C5       C2       C3         E2        E3       C6         E1        E4        E6
FUN_000001 1.852257 1.968200 1.865816 1.607073  1.4506955 1.7721552 1.740823  0.9550386  1.668001  1.506102
FUN_000002 1.641073 1.784428 1.819211 1.607073 -0.4228263 0.2560804 1.233031 -1.2137810 -2.565210 -1.352602
FUN_000004 3.979975 3.856899 4.207481 4.542740  3.4514862 3.5382602 4.089759  3.0178677  3.419495  3.491130
FUN_000005 5.931045 6.097904 5.822537 6.186312  5.9772247 6.2421614 6.552750  6.0750260  6.358862  6.204477
FUN_000006 3.153171 3.462848 3.357371 3.560510  3.5336498 3.5276112 3.685058  3.6727947  3.845697  3.697070
FUN_000007 7.224131 7.386562 7.006085 7.246314  6.4221814 6.3366972 7.260962  6.2908759  6.830785  6.628730
                 C4       C5       C2       C3         E2        E3       C6        E1        E4        E6
FUN_000001 1.960613 1.952141 1.879967 1.504436  1.4656018 1.7474102 1.739188  1.012712  1.609685  1.514177
FUN_000002 1.748630 1.768475 1.833341 1.504436 -0.4103117 0.2338702 1.231460 -1.174707 -2.563982 -1.347231
FUN_000004 4.092234 3.840267 4.222133 4.434596  3.4670652 3.5125520 4.088002  3.079555  3.358777  3.499527
FUN_000005 6.044161 6.081105 5.837272 6.077603  5.9929889 6.2161135 6.550968  6.137823  6.297261  6.212967
FUN_000006 3.264535 3.446283 3.371925 3.453175  3.5492413 3.5019060 3.683310  3.734942  3.784720  3.705482
FUN_000007 7.337424 7.369736 7.020842 7.137468  6.4379559 6.3106453 7.259178  6.353694  6.769147  6.637225

Currently visualizing libraries for pipeline: pipeline_filtered_strgtieh 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000001   46   69   49   47   79   76   59   52   64   90
FUN_000002   34   43   50   40   14   19   34    5    0    6
FUN_000004  250  284  299  361  322  261  324  231  237  374
FUN_000005 1036 1425 1017 1209 2053 1950 2023 2160 2042 2645
FUN_000006  170  238  192  213  411  329  302  418  374  483
13576 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 20.83889 19.73533 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                 C4       C5       C2       C3         E2          E3        C6        E1        E4        E6
FUN_000001 1.734860 1.944150 1.732811 1.740387  1.5537168  1.85136374 1.7453183  1.020492  1.647022  1.605230
FUN_000002 1.313369 1.283484 1.761124 1.514960 -0.7388392 -0.03788253 0.9801896 -1.827232 -3.381207 -1.773957
FUN_000004 4.142732 3.957873 4.306844 4.645151  3.5448135  3.60388406 4.1683453  3.117918  3.503126  3.625292
FUN_000005 6.187797 6.277720 6.067999 6.385008  6.2073764  6.49533872 6.8042939  6.328670  6.599315  6.437784
FUN_000006 3.590023 3.704664 3.671699 3.887845  3.8943198  3.93556614 4.0674609  3.966375  4.156797  3.991743
FUN_000007 7.193788 7.345486 6.909872 7.224163  6.4294008  6.32199862 7.2289649  6.277748  6.795043  6.610132
                 C4       C5       C2       C3         E2         E3        C6        E1        E4        E6
FUN_000001 1.824876 1.938910 1.737251 1.662674  1.5707049  1.8358344 1.7643075  1.064711  1.576789  1.598698
FUN_000002 1.402505 1.278322 1.765567 1.437646 -0.7240795 -0.0522592 0.9987897 -1.796484 -3.381216 -1.778488
FUN_000004 4.234849 3.952532 4.311394 4.565390  3.5622289  3.5880549 4.1877868  3.163793  3.431247  3.618599
FUN_000005 6.280283 6.272352 6.072565 6.305026  6.2249128  6.4794000 6.8238225  6.374995  6.526880  6.431046
FUN_000006 3.681912 3.699330 3.676236 3.808302  3.9117661  3.9197110 4.0868949  4.012475  4.084689  3.985038
FUN_000007 7.286334 7.340115 6.914440 7.144139  6.4469404  6.3060621 7.2484977  6.324072  6.722598  6.603394

Currently visualizing libraries for pipeline: pipeline_filtered_strgties 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000001   53   64   54   42   76   77   66   51   65   91
FUN_000002   45   63   47   50   19   22   43    7    0    6
FUN_000004  241  273  287  341  318  258  307  221  231  356
FUN_000005 1021 1409 1010 1166 2079 1994 2024 2187 2023 2599
FUN_000006  154  215  181  189  385  322  294  386  351  437
13997 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 21.92688 20.86783 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                 C4       C5       C2       C3         E2         E3       C6         E1        E4        E6
FUN_000001 1.872342 1.791387 1.805609 1.540771  1.4219544 1.76168400 1.802909  0.9085955  1.587061  1.567385
FUN_000002 1.642650 1.769270 1.610906 1.785055 -0.4377604 0.04822328 1.204812 -1.5719642 -3.454629 -1.833387
FUN_000004 4.028998 3.854742 4.184740 4.521869  3.4490436 3.47863963 3.990696  2.9691167  3.384618  3.501888
FUN_000005 6.105704 6.215089 5.994784 6.291534  6.1475922 6.41854803 6.704555  6.2607653  6.503946  6.359835
FUN_000006 3.387441 3.512630 3.523911 3.675083  3.7227769 3.79598108 3.928639  3.7664698  3.983883  3.795489
FUN_000007 7.084126 7.266950 6.828320 7.140295  6.3381845 6.23292009 7.148016  6.1647365  6.688282  6.511290
                 C4       C5       C2       C3         E2        E3       C6        E1        E4        E6
FUN_000001 1.956602 1.780767 1.808150 1.449538  1.4349751 1.7541422 1.829495  0.959520  1.521920  1.564770
FUN_000002 1.726553 1.758655 1.613442 1.693342 -0.4258432 0.0412254 1.231050 -1.532551 -3.453562 -1.834883
FUN_000004 4.114858 3.843881 4.187313 4.427939  3.4623809 3.4709317 4.017812  3.021943  3.317966  3.499187
FUN_000005 6.191918 6.204166 5.997362 6.197328  6.1610165 6.4107770 6.731798  6.314129  6.436753  6.357108
FUN_000006 3.473043 3.501788 3.526479 3.581466  3.7361320 3.7882588 3.955749  3.819550  3.917023  3.792782
FUN_000007 7.170394 7.256019 6.830899 7.046038  6.3516108 6.2251503 7.175265  6.218096  6.621080  6.508562

Currently visualizing libraries for pipeline: soft_filtered_htsh 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000001    0   11    8   10   12    7    7    4    5   19
FUN_000002    1    9    8   10    1    2    7    0    0    1
FUN_000003    0    4    4    8    3    5    1    2    0    1
FUN_000004  269  325  338  388  341  271  351  249  264  441
FUN_000005 1114 1569 1092 1321 2181 2014 2129 2293 2210 2940
16526 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 21.79647 20.69348 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4         C5         C2         C3         E2        E3        C6        E1        E4         E6
FUN_000001 -3.446023 -0.6253328 -0.7914303 -0.4447163 -0.9779756 -1.267020 -1.080134 -2.087077 -1.638181 -0.5735877
FUN_000002 -2.656603 -0.8701600 -0.7914303 -0.4447163 -2.9837136 -2.440181 -1.080134 -3.446023 -3.446023 -3.0315738
FUN_000003 -3.446023 -1.7654935 -1.5787816 -0.7222899 -2.3529793 -1.630329 -2.773798 -2.612130 -3.446023 -3.0315738
FUN_000004  4.175546  4.0473783  4.3663788  4.6461065  3.5742476  3.658310  4.263603  3.174854  3.609936  3.7610910
FUN_000005  6.220049  6.3123500  6.0538181  6.4098695  6.2419870  6.542906  6.858469  6.364746  6.665794  6.4897334
FUN_000006  3.706825  3.7973637  3.7761169  4.0206200  4.0279653  4.069400  4.183710  4.096620  4.289365  4.1515467
                  C4         C5         C2         C3         E2        E3        C6        E1        E4         E6
FUN_000001 -3.449469 -0.6241997 -0.7707766 -0.4973256 -0.9745525 -1.293222 -1.066422 -2.060730 -1.698710 -0.5985501
FUN_000002 -2.611142 -0.8691663 -0.7707766 -0.4973256 -2.9848574 -2.458247 -1.066422 -3.449469 -3.449469 -3.0412093
FUN_000003 -3.449469 -1.7652684 -1.5614071 -0.7733858 -2.3519676 -1.654670 -2.769290 -2.594079 -3.449469 -3.0412093
FUN_000004  4.285007  4.0492356  4.3914117  4.5865531  3.5791174  3.625808  4.281313  3.219436  3.526559  3.7328666
FUN_000005  6.329930  6.3142307  6.0789379  6.3501676  6.2469110  6.510219  6.876263  6.409760  6.581870  6.4613644
FUN_000006  3.816074  3.7992154  3.8010861  3.9611808  4.0328525  4.036845  4.201414  4.141431  4.205755  4.1232819

Currently visualizing libraries for pipeline: soft_filtered_htss 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000001    0    0    2    1    4    0    2    0    0    2
FUN_000002    1    0    0    0    1    1    1    0    0    0
FUN_000003    0    4    1    3    3    5    1    2    0    0
FUN_000004  256  316  329  360  344  270  330  239  270  418
FUN_000005 1122 1597 1124 1299 2272 2140 2197 2417 2220 2928
16526 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 23.28954 22.01914 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4        C5        C2        C3        E2        E3        C6        E1        E4        E6
FUN_000001 -3.541611 -3.541611 -2.316809 -2.757821 -2.218154 -3.541611 -2.430871 -3.541611 -3.541611 -2.793250
FUN_000002 -2.748496 -3.541611 -3.541611 -3.541611 -3.081484 -2.969675 -2.881885 -3.541611 -3.541611 -3.541611
FUN_000003 -3.541611 -1.835524 -2.802958 -1.879431 -2.452799 -1.762316 -2.881885 -2.715675 -3.541611 -3.541611
FUN_000004  4.017573  3.948293  4.246128  4.485151  3.483284  3.506707  4.045814  3.002747  3.535832  3.619225
FUN_000005  6.143513  6.279209  6.013985  6.332480  6.197335  6.483731  6.774423  6.326885  6.565947  6.418897
FUN_000006  3.464117  3.553942  3.553567  3.765082  3.812964  3.882702  4.015045  3.829788  4.055677  3.878861
                  C4        C5        C2        C3        E2        E3        C6        E1        E4        E6
FUN_000001 -3.544860 -3.544860 -2.288180 -2.798684 -2.219245 -3.544860 -2.427624 -3.544860 -3.544860 -2.803442
FUN_000002 -2.694452 -3.544860 -3.544860 -3.544860 -3.083751 -2.984433 -2.880692 -3.544860 -3.544860 -3.544860
FUN_000003 -3.544860 -1.845762 -2.783808 -1.944579 -2.454144 -1.790601 -2.880692 -2.697787 -3.544860 -3.544860
FUN_000004  4.145632  3.935012  4.297898  4.390853  3.483600  3.468274  4.054577  3.047033  3.468780  3.598880
FUN_000005  6.272089  6.265882  6.065929  6.237920  6.197674  6.445061  6.783239  6.371628  6.498468  6.398448
FUN_000006  3.591864  3.540678  3.605186  3.671017  3.813286  3.844206  4.023807  3.874296  3.988478  3.858497

Currently visualizing libraries for pipeline: soft_filtered_kallisto 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
            C4  C5  C2  C3   E2   E3   C6   E1   E4   E6
FUN_000001  29  42  33  26   44   39   34   29   37   51
FUN_000002  26  36  33  30   10   12   23    5    0    4
FUN_000003  10  19  20   7    8   15    6   22   12   17
FUN_000004 149 175 181 208  190  141  189  136  148  240
FUN_000005 637 920 634 766 1255 1151 1212 1312 1237 1681
16526 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 13.68348 12.97468 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4        C5       C2         C3         E2         E3         C6         E1         E4
FUN_000001 1.6887986 1.8038949 1.744505  1.4831342  1.3749622 1.61244263  1.6294361  0.8525418  1.5049067
FUN_000002 1.5387847 1.5915329 1.744505  1.6794926 -0.5096350 0.05937857  1.0977684 -1.2099441 -2.7743635
FUN_000003 0.2719566 0.7308194 1.062375 -0.2184983 -0.7583979 0.34025532 -0.5856385  0.4906791  0.0274524
FUN_000004 3.9963230 3.8161445 4.147560  4.4155762  3.4215187 3.41582088  4.0472097  2.9870894  3.4480773
FUN_000005 6.0821462 6.1982494 5.947527  6.2891214  6.1283366 6.42749901  6.7173350  6.2331409  6.4941432
FUN_000006 3.5237985 3.6244722 3.560640  3.8593753  3.8632188 3.84102062  3.9290099  3.8532321  4.0330110
                    E6
FUN_000001  1.38930045
FUN_000002 -1.55575603
FUN_000003 -0.04303163
FUN_000004  3.55893835
FUN_000005  6.35173206
FUN_000006  3.96502259
                  C4        C5       C2         C3         E2         E3         C6         E1          E4
FUN_000001 1.8027055 1.7964958 1.772086  1.3864811  1.3806353 1.57928698  1.6354343  0.9016173  1.45120631
FUN_000002 1.6521147 1.5841754 1.772086  1.5821482 -0.5050991 0.02933613  1.1036032 -1.1748192 -2.77577728
FUN_000003 0.3770466 0.7237096 1.089164 -0.3029676 -0.7541556 0.30937807 -0.5809764  0.5385083 -0.02109785
FUN_000004 4.1144239 3.8085483 4.176202  4.3141953  3.4275117 3.38151523  4.0535058  3.0395357  3.39223611
FUN_000005 6.2010573 6.1906008 5.976345  6.1872214  6.1344162 6.39278862  6.7236889  6.2864753  6.43764005
FUN_000006 3.6414885 3.6168853 3.589158  3.7583301  3.8692388 3.80659705  3.9353001  3.9061266  3.97691883
                    E6
FUN_000001  1.37710908
FUN_000002 -1.56366989
FUN_000003 -0.05413738
FUN_000004  3.54624970
FUN_000005  6.33892178
FUN_000006  3.95229907

Currently visualizing libraries for pipeline: soft_filtered_salmon 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
            C4  C5  C2  C3  E2  E3  C6   E1  E4   E6
FUN_000001  28  40  30  24  40  38  32   27  36   48
FUN_000002  24  35  29  24   9  12  22    4   0    4
FUN_000003   2   3   4   0   0   1   1    2   0    4
FUN_000004 127 153 158 193 168 134 170  121 126  199
FUN_000005 495 730 487 606 980 884 945 1026 980 1322
16526 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 11.83815 11.19564 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4        C5         C2        C3         E2         E3        C6         E1        E4        E6
FUN_000001  1.852098  1.967996  1.8655796  1.606875  1.4505435  1.7720015  1.740670  0.9549101  1.667857  1.505951
FUN_000002  1.640914  1.784223  1.8189745  1.606875 -0.4229801  0.2559258  1.232878 -1.2139209 -2.565372 -1.352757
FUN_000003 -1.269677 -1.152989 -0.6613076 -2.565372 -2.5653725 -1.9749715 -1.899156 -1.7368679 -2.565372 -1.352757
FUN_000004  3.979816  3.856693  4.2072412  4.542541  3.4513347  3.5381069  4.089607  3.0177416  3.419353  3.490979
FUN_000005  5.930885  6.097698  5.8222965  6.186113  5.9770733  6.2420082  6.552598  6.0749006  6.358720  6.204327
FUN_000006  3.153011  3.462642  3.3571324  3.560311  3.5334983  3.5274579  3.684906  3.6726690  3.845555  3.696919
                  C4        C5         C2        C3         E2         E3        C6        E1        E4        E6
FUN_000001  1.957936  1.947997  1.8726283  1.501562  1.4666040  1.7515394  1.741928  1.014232  1.611659  1.518373
FUN_000002  1.745976  1.764360  1.8260150  1.501562 -0.4093928  0.2376451  1.234150 -1.173511 -2.563458 -1.344482
FUN_000003 -1.202273 -1.165335 -0.6554462 -2.563458 -2.5634579 -1.9809235 -1.897497 -1.707193 -2.563458 -1.344482
FUN_000004  4.089445  3.835966  4.2144898  4.431534  3.4680908  3.5168160  4.090837  3.081144  3.360812  3.503895
FUN_000005  6.041347  6.076759  5.8295783  6.074522  5.9940208  6.2204250  6.553822  6.139431  6.299317  6.217385
FUN_000006  3.261771  3.442000  3.3643416  3.450140  3.5502672  3.5061696  3.686138  3.736540  3.786761  3.709858

Currently visualizing libraries for pipeline: soft_filtered_strgtieh 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000001   46   69   49   47   79   76   59   52   64   90
FUN_000002   34   43   50   40   14   19   34    5    0    6
FUN_000003    0    7    0    0    5    4    0    3    0    0
FUN_000004  250  284  299  361  322  261  324  231  237  374
FUN_000005 1036 1425 1017 1209 2053 1950 2023 2160 2042 2645
16526 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 20.8421 19.73807 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4        C5        C2        C3         E2          E3         C6        E1        E4        E6
FUN_000001  1.734773  1.943937  1.732630  1.740151  1.5535359  1.85113886  1.7449169  1.020312  1.646831  1.604936
FUN_000002  1.313281  1.283271  1.760943  1.514724 -0.7390255 -0.03810722  0.9797919 -1.827425 -3.381429 -1.774229
FUN_000003 -3.381429 -1.069286 -3.381429 -3.381429 -1.8589948 -1.83240638 -3.3814291 -2.269205 -3.381429 -3.381429
FUN_000004  4.142648  3.957659  4.306664  4.644914  3.5446336  3.60365913  4.1679395  3.117739  3.502936  3.624997
FUN_000005  6.187713  6.277506  6.067819  6.384771  6.2071968  6.49511378  6.8038874  6.328492  6.599125  6.437488
FUN_000006  3.589939  3.704451  3.671518  3.887608  3.8941400  3.93534121  4.0670552  3.966197  4.156607  3.991447
                  C4        C5        C2        C3         E2          E3         C6        E1        E4        E6
FUN_000001  1.824406  1.938381  1.736096  1.662011  1.5710385  1.83599322  1.7644851  1.064448  1.576220  1.599519
FUN_000002  1.402039  1.277800  1.764411  1.436986 -0.7238032 -0.05212018  0.9989616 -1.796699 -3.381322 -1.777952
FUN_000003 -3.381322 -1.073815 -3.381322 -3.381322 -1.8471411 -1.84259606 -3.3813216 -2.244096 -3.381322 -3.381322
FUN_000004  4.234371  3.951995  4.310214  4.564712  3.5625735  3.58821882  4.1879712  3.163525  3.430666  3.619443
FUN_000005  6.279804  6.271813  6.071380  6.304346  6.2252605  6.47956584  6.8240081  6.374726  6.526296  6.431896
FUN_000006  3.681435  3.698793  3.675058  3.807626  3.9121115  3.91987535  4.0870792  4.012206  4.084107  3.985884

Currently visualizing libraries for pipeline: soft_filtered_strgties 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000001   53   64   54   42   76   77   66   51   65   91
FUN_000002   45   63   47   50   19   22   43    7    0    6
FUN_000003    0    0    0    0    3    4    3    0    0    0
FUN_000004  241  273  287  341  318  258  307  221  231  356
FUN_000005 1021 1409 1010 1166 2079 1994 2024 2187 2023 2599
16526 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 21.9297 20.87003 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4        C5        C2        C3         E2         E3        C6         E1        E4        E6
FUN_000001  1.872229  1.791210  1.805480  1.540516  1.4217463  1.7615268  1.802781  0.9084199  1.586886  1.567113
FUN_000002  1.642537  1.769093  1.610777  1.784800 -0.4379664  0.0480643  1.204684 -1.5721421 -3.454814 -1.833633
FUN_000003 -3.454814 -3.454814 -3.454814 -3.454814 -2.3706815 -1.9290821 -2.025369 -3.4548144 -3.454814 -3.454814
FUN_000004  4.028886  3.854566  4.184612  4.521612  3.4488349  3.4784830  3.990569  2.9689414  3.384444  3.501614
FUN_000005  6.105593  6.214913  5.994656  6.291277  6.1473833  6.4183916  6.704429  6.2605902  6.503771  6.359561
FUN_000006  3.387330  3.512453  3.523782  3.674826  3.7225682  3.7958245  3.928513  3.7662946  3.983708  3.795215
                  C4        C5        C2        C3         E2          E3        C6         E1        E4        E6
FUN_000001  1.982971  1.776073  1.822322  1.433315  1.4312203  1.73800380  1.820266  0.9596577  1.534442  1.559655
FUN_000002  1.752808  1.753962  1.627553  1.677038 -0.4293771  0.02602827  1.221927 -1.5327531 -3.454917 -1.838853
FUN_000003 -3.454917 -3.454917 -3.454917 -3.454917 -2.3655382 -1.94485152 -2.014092 -3.4549168 -3.454917 -3.454917
FUN_000004  4.141740  3.839117  4.201818  4.411265  3.4585627  3.45450577  4.008420  3.0221363  3.330810  3.493983
FUN_000005  6.218913  6.199385  6.011924  6.180607  6.1571809  6.39424165  6.722367  6.3143387  6.449712  6.351877
FUN_000006  3.499842  3.497031  3.540938  3.564843  3.7323102  3.77180805  3.946359  3.8197513  3.929911  3.787572

Currently visualizing libraries for pipeline: unfiltered_htsh 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000001    0   11    8   10   12    7    7    4    5   19
FUN_000002    1    9    8   10    1    2    7    0    0    1
FUN_000003    0    4    4    8    3    5    1    2    0    1
FUN_000004  269  325  338  388  341  271  351  249  264  441
FUN_000005 1114 1569 1092 1321 2181 2014 2129 2293 2210 2940
16607 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 21.79658 20.69361 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4         C5         C2         C3         E2        E3        C6        E1        E4         E6
FUN_000001 -3.446030 -0.6253442 -0.7914398 -0.4447273 -0.9779806 -1.267027 -1.080141 -2.087083 -1.638186 -0.5735966
FUN_000002 -2.656610 -0.8701713 -0.7914398 -0.4447273 -2.9837201 -2.440188 -1.080141 -3.446030 -3.446030 -3.0315815
FUN_000003 -3.446030 -1.7655041 -1.5787908 -0.7223007 -2.3529851 -1.630336 -2.773805 -2.612136 -3.446030 -3.0315815
FUN_000004  4.175540  4.0473663  4.3663689  4.6460950  3.5742432  3.658303  4.263596  3.174850  3.609932  3.7610819
FUN_000005  6.220043  6.3123380  6.0538082  6.4098580  6.2419825  6.542900  6.858461  6.364742  6.665789  6.4897243
FUN_000006  3.706819  3.7973517  3.7761070  4.0206085  4.0279609  4.069393  4.183702  4.096616  4.289361  4.1515376
                  C4         C5         C2         C3         E2        E3        C6        E1        E4         E6
FUN_000001 -3.450805 -0.6262679 -0.7505176 -0.5128048 -0.9760856 -1.301912 -1.079185 -2.066231 -1.693835 -0.6015904
FUN_000002 -2.599801 -0.8712123 -0.7505176 -0.5128048 -2.9862598 -2.464290 -1.079185 -3.450805 -3.450805 -3.0430330
FUN_000003 -3.450805 -1.7671916 -1.5440464 -0.7884203 -2.3534317 -1.662754 -2.775929 -2.598427 -3.450805 -3.0430330
FUN_000004  4.312144  4.0470518  4.4155221  4.5690256  3.5775430  3.615055  4.265894  3.211422  3.533983  3.7295655
FUN_000005  6.357169  6.3120432  6.1031245  6.3325962  6.2453351  6.499404  6.860788  6.401687  6.589355  6.4580518
FUN_000006  3.843160  3.7970325  3.8251407  3.9436871  4.0312776  4.026074  4.185998  4.133386  4.213205  4.1199776

Currently visualizing libraries for pipeline: unfiltered_htss 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000001    0    0    2    1    4    0    2    0    0    2
FUN_000002    1    0    0    0    1    1    1    0    0    0
FUN_000003    0    4    1    3    3    5    1    2    0    0
FUN_000004  256  316  329  360  344  270  330  239  270  418
FUN_000005 1122 1597 1124 1299 2272 2140 2197 2417 2220 2928
16607 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 23.28968 22.01927 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4        C5        C2        C3        E2        E3        C6        E1        E4        E6
FUN_000001 -3.541619 -3.541619 -2.316819 -2.757832 -2.218161 -3.541619 -2.430880 -3.541619 -3.541619 -2.793259
FUN_000002 -2.748503 -3.541619 -3.541619 -3.541619 -3.081492 -2.969683 -2.881894 -3.541619 -3.541619 -3.541619
FUN_000003 -3.541619 -1.835536 -2.802967 -1.879444 -2.452806 -1.762323 -2.881894 -2.715682 -3.541619 -3.541619
FUN_000004  4.017567  3.948279  4.246117  4.485137  3.483278  3.506700  4.045804  3.002742  3.535827  3.619214
FUN_000005  6.143508  6.279195  6.013974  6.332465  6.197329  6.483724  6.774413  6.326881  6.565942  6.418886
FUN_000006  3.464111  3.553929  3.553556  3.765067  3.812958  3.882694  4.015035  3.829784  4.055672  3.878851
                  C4        C5        C2        C3        E2        E3        C6        E1        E4        E6
FUN_000001 -3.544792 -3.544792 -2.288806 -2.798798 -2.219015 -3.544792 -2.427515 -3.544792 -3.544792 -2.803280
FUN_000002 -2.694400 -3.544792 -3.544792 -3.544792 -3.083609 -2.984327 -2.880595 -3.544792 -3.544792 -3.544792
FUN_000003 -3.544792 -1.845805 -2.784229 -1.944814 -2.453932 -1.790452 -2.880595 -2.697563 -3.544792 -3.544792
FUN_000004  4.145664  3.934921  4.296778  4.390470  3.483936  3.468456  4.054721  3.047449  3.468880  3.599179
FUN_000005  6.272120  6.265791  6.064805  6.237535  6.198012  6.445244  6.783382  6.372047  6.498568  6.398749
FUN_000006  3.591895  3.540588  3.604068  3.670635  3.813622  3.844388  4.023950  3.874713  3.988578  3.858796

Currently visualizing libraries for pipeline: unfiltered_kallisto 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
            C4  C5  C2  C3   E2   E3   C6   E1   E4   E6
FUN_000001  29  42  33  26   44   39   34   29   37   51
FUN_000002  26  36  33  30   10   12   23    5    0    4
FUN_000003  10  19  20   7    8   15    6   22   12   17
FUN_000004 149 175 181 208  190  141  189  136  148  240
FUN_000005 637 920 634 766 1255 1151 1212 1312 1237 1681
16607 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 13.68358 12.9748 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4        C5       C2         C3         E2         E3         C6         E1          E4
FUN_000001 1.6887905 1.8038794 1.744491  1.4831162  1.3749540 1.61243153  1.6294228  0.8525346  1.50489912
FUN_000002 1.5387766 1.5915175 1.744491  1.6794746 -0.5096436 0.05936747  1.0977552 -1.2099523 -2.77437462
FUN_000003 0.2719482 0.7308042 1.062361 -0.2185154 -0.7584066 0.34024423 -0.5856514  0.4906719  0.02744451
FUN_000004 3.9963150 3.8161289 4.147546  4.4155579  3.4215107 3.41580978  4.0471963  2.9870825  3.44806991
FUN_000005 6.0821382 6.1982338 5.947513  6.2891030  6.1283287 6.42748791  6.7173216  6.2331342  6.49413582
FUN_000006 3.5237905 3.6244566 3.560626  3.8593570  3.8632108 3.84100953  3.9289965  3.8532253  4.03300364
                    E6
FUN_000001  1.38928774
FUN_000002 -1.55576811
FUN_000003 -0.04304418
FUN_000004  3.55892556
FUN_000005  6.35171926
FUN_000006  3.96500981
                  C4        C5       C2        C3         E2         E3         C6         E1          E4
FUN_000001 1.8026040 1.7964925 1.771523  1.386109  1.3809247 1.57949033  1.6351640  0.9018512  1.45106738
FUN_000002 1.6520140 1.5841726 1.771523  1.581773 -0.5048446 0.02952635  1.1033404 -1.1746298 -2.77570644
FUN_000003 0.3769578 0.7237099 1.088618 -0.303281 -0.7539102 0.30957181 -0.5811853  0.5387383 -0.02121577
FUN_000004 4.1143164 3.8085425 4.175616  4.313801  3.4278110 3.38172343  4.0532218  3.0397803  3.39208843
FUN_000005 6.2009486 6.1905943 5.975755  6.186825  6.1347181 6.39299853  6.7234023  6.2867227  6.43748966
FUN_000006 3.6413816 3.6168796 3.588575  3.757937  3.8695389 3.80680575  3.9350165  3.9063726  3.97677012
                    E6
FUN_000001  1.37774261
FUN_000002 -1.56326016
FUN_000003 -0.05356071
FUN_000004  3.54690929
FUN_000005  6.33958775
FUN_000006  3.95296049

Currently visualizing libraries for pipeline: unfiltered_salmon 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
            C4  C5  C2  C3  E2  E3  C6   E1  E4   E6
FUN_000001  28  40  30  24  40  38  32   27  36   48
FUN_000002  24  35  29  24   9  12  22    4   0    4
FUN_000003   2   3   4   0   0   1   1    2   0    4
FUN_000004 127 153 158 193 168 134 170  121 126  199
FUN_000005 495 730 487 606 980 884 945 1026 980 1322
16607 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 11.83823 11.19573 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4        C5        C2        C3         E2         E3        C6         E1        E4        E6
FUN_000001  1.852090  1.967983  1.865569  1.606861  1.4505359  1.7719914  1.740658  0.9549038  1.667850  1.505940
FUN_000002  1.640906  1.784211  1.818964  1.606861 -0.4229881  0.2559158  1.232867 -1.2139283 -2.565382 -1.352768
FUN_000003 -1.269686 -1.153001 -0.661318 -2.565382 -2.5653821 -1.9749813 -1.899166 -1.7368759 -2.565382 -1.352768
FUN_000004  3.979808  3.856680  4.207230  4.542527  3.4513272  3.5380968  4.089595  3.0177356  3.419346  3.490967
FUN_000005  5.930878  6.097685  5.822286  6.186098  5.9770659  6.2419981  6.552586  6.0748947  6.358713  6.204315
FUN_000006  3.153004  3.462630  3.357122  3.560297  3.5334909  3.5274478  3.684894  3.6726630  3.845548  3.696908
                  C4        C5         C2        C3         E2        E3        C6        E1        E4        E6
FUN_000001  1.958079  1.947846  1.8728633  1.501280  1.4666787  1.751631  1.741217  1.014309  1.611731  1.518737
FUN_000002  1.746118  1.764210  1.8262497  1.501280 -0.4093273  0.237730  1.233457 -1.173452 -2.563436 -1.344253
FUN_000003 -1.202174 -1.165426 -0.6552604 -2.563436 -2.5634361 -1.980877 -1.897761 -1.707144 -2.563436 -1.344253
FUN_000004  4.089592  3.835809  4.2147331  4.431236  3.4681681  3.516910  4.090095  3.081226  3.360886  3.504275
FUN_000005  6.041495  6.076600  5.8298229  6.074222  5.9940989  6.220520  6.553074  6.139514  6.299392  6.217769
FUN_000006  3.261918  3.441844  3.3645832  3.449844  3.5503446  3.506264  3.685398  3.736622  3.786835  3.710239

Currently visualizing libraries for pipeline: unfiltered_strgtieh 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000001   46   69   49   47   79   76   59   52   64   90
FUN_000002   34   43   50   40   14   19   34    5    0    6
FUN_000003    0    7    0    0    5    4    0    3    0    0
FUN_000004  250  284  299  361  322  261  324  231  237  374
FUN_000005 1036 1425 1017 1209 2053 1950 2023 2160 2042 2645
16607 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 20.84221 19.73819 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4        C5        C2        C3         E2          E3         C6        E1        E4        E6
FUN_000001  1.734767  1.943924  1.732622  1.740139  1.5535311  1.85113029  1.7449093  1.020308  1.646826  1.604927
FUN_000002  1.313274  1.283259  1.760935  1.514712 -0.7390307 -0.03811572  0.9797843 -1.827430 -3.381437 -1.774238
FUN_000003 -3.381437 -1.069297 -3.381437 -3.381437 -1.8590005 -1.83241463 -3.3814366 -2.269211 -3.381437 -3.381437
FUN_000004  4.142641  3.957647  4.306656  4.644902  3.5446289  3.60365054  4.1679320  3.117735  3.502931  3.624988
FUN_000005  6.187707  6.277494  6.067811  6.384759  6.2071921  6.49510518  6.8038798  6.328488  6.599120  6.437479
FUN_000006  3.589932  3.704439  3.671510  3.887597  3.8941353  3.93533261  4.0670477  3.966192  4.156602  3.991438
                  C4        C5        C2        C3         E2          E3         C6        E1        E4        E6
FUN_000001  1.824369  1.938344  1.736061  1.661973  1.5710855  1.83585864  1.7645157  1.064406  1.576263  1.599647
FUN_000002  1.402002  1.277763  1.764376  1.436949 -0.7237622 -0.05224458  0.9989915 -1.796728 -3.381320 -1.777862
FUN_000003 -3.381320 -1.073845 -3.381320 -3.381320 -1.8471088 -1.84268617 -3.3813198 -2.244119 -3.381320 -3.381320
FUN_000004  4.234333  3.951957  4.310177  4.564673  3.5626216  3.58808160  4.1880024  3.163481  3.430710  3.619575
FUN_000005  6.279765  6.271775  6.071344  6.304307  6.2253089  6.47942765  6.8240395  6.374681  6.526340  6.432028
FUN_000006  3.681397  3.698755  3.675022  3.807587  3.9121597  3.91973789  4.0871104  4.012161  4.084151  3.986015

Currently visualizing libraries for pipeline: unfiltered_strgties 


Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

An object of class "DGEList"
$counts
             C4   C5   C2   C3   E2   E3   C6   E1   E4   E6
FUN_000001   53   64   54   42   76   77   66   51   65   91
FUN_000002   45   63   47   50   19   22   43    7    0    6
FUN_000003    0    0    0    0    3    4    3    0    0    0
FUN_000004  241  273  287  341  318  258  307  221  231  356
FUN_000005 1021 1409 1010 1166 2079 1994 2024 2187 2023 2599
16607 more rows ...

$samples
NA


 Mean and Median Library Size in Millions: 21.92982 20.87014 
Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

                  C4        C5        C2        C3        E2          E3        C6         E1        E4        E6
FUN_000001  1.872225  1.791198  1.805471  1.540503  1.421741  1.76151751  1.802771  0.9084147  1.586880  1.567103
FUN_000002  1.642533  1.769081  1.610768  1.784786 -0.437972  0.04805507  1.204674 -1.5721480 -3.454823 -1.833642
FUN_000003 -3.454823 -3.454823 -3.454823 -3.454823 -2.370688 -1.92909101 -2.025378 -3.4548226 -3.454823 -3.454823
FUN_000004  4.028882  3.854554  4.184603  4.521598  3.448830  3.47847366  3.990559  2.9689364  3.384438  3.501604
FUN_000005  6.105589  6.214900  5.994647  6.291264  6.147378  6.41838226  6.704419  6.2605851  6.503765  6.359551
FUN_000006  3.387326  3.512441  3.523773  3.674812  3.722563  3.79581515  3.928503  3.7662895  3.983702  3.795205
                  C4        C5        C2        C3         E2          E3        C6         E1        E4        E6
FUN_000001  1.982824  1.776361  1.821980  1.433410  1.4313182  1.73800875  1.820165  0.9598587  1.534410  1.559501
FUN_000002  1.752662  1.754250  1.627213  1.677134 -0.4292879  0.02603313  1.221828 -1.5325971 -3.454913 -1.838959
FUN_000003 -3.454913 -3.454913 -3.454913 -3.454913 -2.3654829 -1.94484703 -2.014156 -3.4549133 -3.454913 -3.454913
FUN_000004  4.141591  3.839411  4.201469  4.411362  3.4586632  3.45451074  4.008317  3.0223447  3.330778  3.493825
FUN_000005  6.218763  6.199681  6.011574  6.180705  6.1572820  6.39424663  6.722264  6.3145491  6.449680  6.351718
FUN_000006  3.499694  3.497324  3.540590  3.564941  3.7324108  3.77181303  3.946256  3.8199607  3.929879  3.787415

Currently proccessing: hard_filtered_htsh with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 14301 10 
metadata(1): version
assays(1): counts
rownames(14301): FUN_000004 FUN_000005 ... FUN_016610 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
             baseMean log2FoldChange     lfcSE      stat      pvalue        padj
            <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000004  329.06335      0.8021298  0.145444  5.515035 3.48711e-08 6.38189e-07
FUN_000005 1790.98771     -0.0162546  0.153936 -0.105593 9.15905e-01 9.54708e-01
FUN_000006  335.22436     -0.1696714  0.113818 -1.490731 1.36032e-01 2.70729e-01
FUN_000007 2491.40424      0.7485811  0.132787  5.637458 1.72578e-08 3.52568e-07
FUN_000008  204.25403      0.6721664  0.313293  2.145488 3.19139e-02 8.78170e-02
FUN_000009    8.50932     -0.4926493  0.560952 -0.878238 3.79815e-01 5.54059e-01

out of 13177 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2694, 20%
LFC < 0 (down)     : 2217, 17%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4172        Min.   :      2   Length:4172        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    157   Class :character   1st Qu.:1.068e-06   1st Qu.:1.348e-05  
 Mode  :character   Median :    441   Mode  :character   Median :2.280e-04   Median :1.440e-03  
                    Mean   :   2815                      Mean   :2.288e-03   Mean   :8.429e-03  
                    3rd Qu.:   1184                      3rd Qu.:2.950e-03   3rd Qu.:1.242e-02  
                    Max.   :3264274                      Max.   :1.576e-02   Max.   :4.978e-02  

Currently proccessing: hard_filtered_htss with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 14301 10 
metadata(1): version
assays(1): counts
rownames(14301): FUN_000004 FUN_000005 ... FUN_016610 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
-- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000004  317.8861      0.7586462  0.132790  5.713123 1.10921e-08 1.83242e-07
FUN_000005 1829.7844     -0.0319764  0.138075 -0.231588 8.16858e-01 8.88088e-01
FUN_000006  305.1968     -0.1707248  0.110897 -1.539484 1.23686e-01 2.34155e-01
FUN_000007 2468.9024      0.7468472  0.120586  6.193477 5.88513e-10 1.28135e-08
FUN_000008  230.9897      0.6546024  0.290035  2.256975 2.40096e-02 6.36277e-02
FUN_000009   86.8491     -0.3421158  0.184391 -1.855384 6.35414e-02 1.39172e-01

out of 13216 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2969, 22%
LFC < 0 (down)     : 2551, 19%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4730        Min.   :      3   Length:4730        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    176   Class :character   1st Qu.:6.020e-07   1st Qu.:6.720e-06  
 Mode  :character   Median :    473   Mode  :character   Median :1.993e-04   Median :1.114e-03  
                    Mean   :   2898                      Mean   :2.458e-03   Mean   :7.952e-03  
                    3rd Qu.:   1180                      3rd Qu.:3.039e-03   3rd Qu.:1.132e-02  
                    Max.   :3487633                      Max.   :1.788e-02   Max.   :4.995e-02  
Currently proccessing: hard_filtered_kallisto with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 14301 10 
metadata(1): version
assays(1): counts
rownames(14301): FUN_000004 FUN_000005 ... FUN_016610 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
-- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000004   178.117      0.7474916 0.1308573  5.712266 1.11482e-08 1.81571e-07
FUN_000005  1029.223     -0.0340947 0.1402233 -0.243146 8.07893e-01 8.78819e-01
FUN_000006   181.801     -0.1820578 0.0979736 -1.858233 6.31360e-02 1.37388e-01
FUN_000007  1428.859      0.7308336 0.1110079  6.583618 4.59135e-11 1.20684e-09
FUN_000008   128.625      0.7024505 0.2762820  2.542513 1.10059e-02 3.29036e-02
FUN_000009    37.374     -0.2486318 0.2426558 -1.024627 3.05539e-01 4.56551e-01

out of 13274 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 3000, 23%
LFC < 0 (down)     : 2622, 20%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr            logFC                pval              adj.pval        
 Length:4820        Min.   :      2.8   Length:4820        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    115.7   Class :character   1st Qu.:5.030e-07   1st Qu.:5.540e-06  
 Mode  :character   Median :    286.4   Mode  :character   Median :1.653e-04   Median :9.102e-04  
                    Mean   :   1686.1                      Mean   :2.494e-03   Mean   :7.900e-03  
                    3rd Qu.:    684.4                      3rd Qu.:2.936e-03   3rd Qu.:1.078e-02  
                    Max.   :2105672.7                      Max.   :1.815e-02   Max.   :4.998e-02  
Currently proccessing: hard_filtered_salmon with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 14301 10 
metadata(1): version
assays(1): counts
rownames(14301): FUN_000004 FUN_000005 ... FUN_016610 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
-- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000004  157.4772      0.7682161  0.138521  5.545853 2.92525e-08 4.54468e-07
FUN_000005  803.6924     -0.0297947  0.137036 -0.217422 8.27879e-01 8.95893e-01
FUN_000006  131.0924     -0.2010878  0.118626 -1.695146 9.00476e-02 1.85799e-01
FUN_000007 1361.2013      0.7239549  0.117415  6.165802 7.01267e-10 1.52781e-08
FUN_000008  113.9014      0.7237316  0.280859  2.576848 9.97059e-03 3.14485e-02
FUN_000009   45.0502     -0.2687808  0.217626 -1.235060 2.16808e-01 3.61640e-01

out of 13159 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2847, 22%
LFC < 0 (down)     : 2491, 19%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr            logFC                pval              adj.pval        
 Length:4570        Min.   :      0.9   Length:4570        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:     95.5   Class :character   1st Qu.:5.520e-07   1st Qu.:6.350e-06  
 Mode  :character   Median :    241.2   Mode  :character   Median :1.860e-04   Median :1.071e-03  
                    Mean   :   1521.7                      Mean   :2.475e-03   Mean   :8.252e-03  
                    3rd Qu.:    594.2                      3rd Qu.:3.127e-03   3rd Qu.:1.200e-02  
                    Max.   :1999047.9                      Max.   :1.735e-02   Max.   :4.997e-02  
Currently proccessing: hard_filtered_strgtieh with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 14301 10 
metadata(1): version
assays(1): counts
rownames(14301): FUN_000004 FUN_000005 ... FUN_016610 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000004   301.090      0.7965256  0.147325  5.406573 6.42420e-08 1.08756e-06
FUN_000005  1671.825     -0.0272128  0.145128 -0.187509 8.51262e-01 9.15985e-01
FUN_000006   294.373     -0.1734708  0.111659 -1.553579 1.20285e-01 2.43667e-01
FUN_000007  2354.575      0.7151740  0.120552  5.932493 2.98369e-09 7.12530e-08
FUN_000008   200.577      0.6417664  0.303446  2.114930 3.44359e-02 9.22481e-02
FUN_000009    18.840     -0.1824421  0.331933 -0.549635 5.82570e-01 7.29573e-01

out of 13137 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2684, 20%
LFC < 0 (down)     : 2330, 18%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4234        Min.   :      3   Length:4234        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    176   Class :character   1st Qu.:1.038e-06   1st Qu.:1.287e-05  
 Mode  :character   Median :    447   Mode  :character   Median :2.157e-04   Median :1.338e-03  
                    Mean   :   2877                      Mean   :2.421e-03   Mean   :8.749e-03  
                    3rd Qu.:   1129                      3rd Qu.:3.198e-03   3rd Qu.:1.323e-02  
                    Max.   :3270758                      Max.   :1.611e-02   Max.   :4.998e-02  
Currently proccessing: hard_filtered_strgties with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 14301 10 
metadata(1): version
assays(1): counts
rownames(14301): FUN_000004 FUN_000005 ... FUN_016610 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000004  289.7407      0.7837389  0.150092  5.221723 1.77266e-07 2.71122e-06
FUN_000005 1662.5022     -0.0392133  0.143688 -0.272906 7.84926e-01 8.71523e-01
FUN_000006  273.6608     -0.1774747  0.121593 -1.459583 1.44405e-01 2.76800e-01
FUN_000007 2320.1885      0.7229205  0.120880  5.980497 2.22458e-09 5.38144e-08
FUN_000008  215.1178      0.6694007  0.300142  2.230283 2.57287e-02 7.29854e-02
FUN_000009   86.5654     -0.2931142  0.178859 -1.638802 1.01255e-01 2.13283e-01

out of 13184 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2724, 21%
LFC < 0 (down)     : 2328, 18%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4265        Min.   :      5   Length:4265        Min.   :0.000e+00   Min.   :0.0000000  
 Class :character   1st Qu.:    195   Class :character   1st Qu.:1.101e-06   1st Qu.:0.0000136  
 Mode  :character   Median :    471   Mode  :character   Median :2.302e-04   Median :0.0014231  
                    Mean   :   2980                      Mean   :2.443e-03   Mean   :0.0088114  
                    3rd Qu.:   1159                      3rd Qu.:3.243e-03   3rd Qu.:0.0133647  
                    Max.   :3399678                      Max.   :1.617e-02   Max.   :0.0499740  
Currently proccessing: pipeline_filtered_htsh with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 13978 10 
metadata(1): version
assays(1): counts
rownames(13978): FUN_000001 FUN_000002 ... FUN_016606 FUN_016608
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
             baseMean log2FoldChange     lfcSE       stat      pvalue        padj
            <numeric>      <numeric> <numeric>  <numeric>   <numeric>   <numeric>
FUN_000001    7.71570      0.3181218  0.651703  0.4881395 6.25451e-01 7.66555e-01
FUN_000002    4.53848      3.7767524  0.991774  3.8080784 1.40051e-04 9.94731e-04
FUN_000003    2.97278      1.2457417  1.083952  1.1492589 2.50449e-01 4.25471e-01
FUN_000004  328.92849      0.8029741  0.145114  5.5334067 3.14070e-08 6.08043e-07
FUN_000005 1790.29090     -0.0151639  0.154915 -0.0978855 9.22023e-01 9.57454e-01
FUN_000006  335.07379     -0.1686332  0.113899 -1.4805514 1.38726e-01 2.80185e-01

out of 13978 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2768, 20%
LFC < 0 (down)     : 2259, 16%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4238        Min.   :      2   Length:4238        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    141   Class :character   1st Qu.:1.076e-06   1st Qu.:1.419e-05  
 Mode  :character   Median :    418   Mode  :character   Median :2.286e-04   Median :1.507e-03  
                    Mean   :   2764                      Mean   :2.209e-03   Mean   :8.508e-03  
                    3rd Qu.:   1160                      3rd Qu.:2.873e-03   3rd Qu.:1.264e-02  
                    Max.   :3262641                      Max.   :1.515e-02   Max.   :4.997e-02  
Currently proccessing: pipeline_filtered_htss with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 14331 10 
metadata(1): version
assays(1): counts
rownames(14331): FUN_000004 FUN_000005 ... FUN_016606 FUN_016608
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000004  317.6672      0.7591278  0.145682  5.210856 1.87971e-07 2.97659e-06
FUN_000005 1828.8132     -0.0317301  0.150706 -0.210543 8.33244e-01 9.04569e-01
FUN_000006  305.0215     -0.1709978  0.123492 -1.384683 1.66149e-01 3.13672e-01
FUN_000007 2467.3683      0.7469253  0.129643  5.761388 8.34252e-09 1.83933e-07
FUN_000008  230.8187      0.6539446  0.309705  2.111507 3.47287e-02 9.58954e-02
FUN_000009   86.8004     -0.3426517  0.192893 -1.776385 7.56695e-02 1.75757e-01

out of 14331 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2888, 20%
LFC < 0 (down)     : 2363, 16%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4490        Min.   :      2   Length:4490        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    151   Class :character   1st Qu.:1.287e-06   1st Qu.:1.643e-05  
 Mode  :character   Median :    440   Mode  :character   Median :2.490e-04   Median :1.589e-03  
                    Mean   :   2970                      Mean   :2.462e-03   Mean   :9.222e-03  
                    3rd Qu.:   1163                      3rd Qu.:3.434e-03   3rd Qu.:1.461e-02  
                    Max.   :3485148                      Max.   :1.564e-02   Max.   :4.991e-02  
Currently proccessing: pipeline_filtered_kallisto with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 14434 10 
metadata(1): version
assays(1): counts
rownames(14434): FUN_000001 FUN_000002 ... FUN_016606 FUN_016608
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001   35.8632      0.3614205  0.214507  1.684891 9.20096e-02 2.03006e-01
FUN_000002   20.8052      2.8891732  0.481351  6.002214 1.94645e-09 4.84398e-08
FUN_000003   13.4965      0.3815455  0.446491  0.854542 3.92805e-01 5.71144e-01
FUN_000004  177.9485      0.7472602  0.143653  5.201831 1.97335e-07 3.07595e-06
FUN_000005 1028.6181     -0.0347702  0.151032 -0.230217 8.17924e-01 8.93778e-01
FUN_000006  181.6794     -0.1830192  0.110417 -1.657530 9.74124e-02 2.12010e-01

out of 14434 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2887, 20%
LFC < 0 (down)     : 2438, 17%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr            logFC                pval              adj.pval        
 Length:4499        Min.   :      2.5   Length:4499        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    103.5   Class :character   1st Qu.:9.970e-07   1st Qu.:1.279e-05  
 Mode  :character   Median :    274.1   Mode  :character   Median :1.999e-04   Median :1.282e-03  
                    Mean   :   1762.0                      Mean   :2.277e-03   Mean   :8.496e-03  
                    3rd Qu.:    690.3                      3rd Qu.:2.867e-03   3rd Qu.:1.226e-02  
                    Max.   :2104523.3                      Max.   :1.554e-02   Max.   :4.987e-02  
Currently proccessing: pipeline_filtered_salmon with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 13668 10 
metadata(1): version
assays(1): counts
rownames(13668): FUN_000001 FUN_000002 ... FUN_016606 FUN_016608
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
-- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001   33.8100      0.3565179  0.210599  1.692879 9.04784e-02 1.88573e-01
FUN_000002   18.9081      2.8462956  0.444522  6.403043 1.52310e-10 3.86229e-09
FUN_000004  157.4409      0.7678860  0.138167  5.557681 2.73383e-08 4.43777e-07
FUN_000005  803.5784     -0.0300373  0.137189 -0.218949 8.26690e-01 8.95457e-01
FUN_000006  131.0752     -0.2014107  0.118556 -1.698870 8.93436e-02 1.86663e-01
FUN_000007 1360.9607      0.7236389  0.117767  6.144678 8.01259e-10 1.78075e-08

out of 13668 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2917, 21%
LFC < 0 (down)     : 2529, 19%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr            logFC                pval              adj.pval        
 Length:4656        Min.   :      2.0   Length:4656        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:     88.4   Class :character   1st Qu.:6.090e-07   1st Qu.:7.150e-06  
 Mode  :character   Median :    235.3   Mode  :character   Median :1.824e-04   Median :1.071e-03  
                    Mean   :   1469.8                      Mean   :2.417e-03   Mean   :8.218e-03  
                    3rd Qu.:    583.4                      3rd Qu.:3.030e-03   3rd Qu.:1.186e-02  
                    Max.   :1998822.1                      Max.   :1.703e-02   Max.   :4.998e-02  
Currently proccessing: pipeline_filtered_strgtieh with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 13581 10 
metadata(1): version
assays(1): counts
rownames(13581): FUN_000001 FUN_000002 ... FUN_016607 FUN_016608
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001   61.6835      0.2616064  0.196726  1.329800 1.83584e-01 3.36933e-01
FUN_000002   28.8965      2.8703467  0.616942  4.652536 3.27877e-06 3.62614e-05
FUN_000004  301.0680      0.7965431  0.146957  5.420249 5.95160e-08 1.04027e-06
FUN_000005 1671.7991     -0.0270175  0.145279 -0.185969 8.52469e-01 9.17747e-01
FUN_000006  294.3629     -0.1733382  0.111632 -1.552760 1.20480e-01 2.46906e-01
FUN_000007 2354.5175      0.7152987  0.120593  5.931516 3.00150e-09 7.25327e-08

out of 13581 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2713, 20%
LFC < 0 (down)     : 2363, 17%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4286        Min.   :      3   Length:4286        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    170   Class :character   1st Qu.:1.040e-06   1st Qu.:1.317e-05  
 Mode  :character   Median :    438   Mode  :character   Median :2.169e-04   Median :1.374e-03  
                    Mean   :   2841                      Mean   :2.370e-03   Mean   :8.753e-03  
                    3rd Qu.:   1113                      3rd Qu.:3.143e-03   3rd Qu.:1.328e-02  
                    Max.   :3270732                      Max.   :1.574e-02   Max.   :4.988e-02  
Currently proccessing: pipeline_filtered_strgties with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 14002 10 
metadata(1): version
assays(1): counts
rownames(14002): FUN_000001 FUN_000002 ... FUN_016607 FUN_016608
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001   62.9472      0.3300023  0.208950  1.579335 1.14259e-01 2.39622e-01
FUN_000002   35.5303      2.8927390  0.618695  4.675546 2.93172e-06 3.33468e-05
FUN_000004  289.7479      0.7839518  0.149676  5.237663 1.62623e-07 2.60830e-06
FUN_000005 1662.6752     -0.0387563  0.143834 -0.269451 7.87583e-01 8.77971e-01
FUN_000006  273.6803     -0.1770701  0.121549 -1.456783 1.45176e-01 2.84926e-01
FUN_000007 2320.3713      0.7232859  0.120562  5.999299 1.98171e-09 5.11955e-08

out of 14002 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2779, 20%
LFC < 0 (down)     : 2359, 17%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4333        Min.   :      2   Length:4333        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    185   Class :character   1st Qu.:1.106e-06   1st Qu.:1.429e-05  
 Mode  :character   Median :    456   Mode  :character   Median :2.165e-04   Median :1.399e-03  
                    Mean   :   2933                      Mean   :2.323e-03   Mean   :8.752e-03  
                    3rd Qu.:   1142                      3rd Qu.:3.026e-03   3rd Qu.:1.304e-02  
                    Max.   :3399846                      Max.   :1.546e-02   Max.   :4.995e-02  
Currently proccessing: soft_filtered_htsh with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 16531 10 
metadata(1): version
assays(1): counts
rownames(16531): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
             baseMean log2FoldChange     lfcSE       stat      pvalue        padj
            <numeric>      <numeric> <numeric>  <numeric>   <numeric>   <numeric>
FUN_000001    7.71570      0.3181787  0.643799  0.4942207 6.21150e-01 7.93285e-01
FUN_000002    4.53848      3.7775642  0.983005  3.8428741 1.21602e-04 9.47956e-04
FUN_000003    2.97278      1.2464273  1.071784  1.1629461 2.44851e-01 4.45155e-01
FUN_000004  328.92849      0.8029715  0.145023  5.5368447 3.07969e-08 6.50258e-07
FUN_000005 1790.29090     -0.0151654  0.155204 -0.0977128 9.22160e-01 9.67069e-01
FUN_000006  335.07379     -0.1686308  0.113793 -1.4819122 1.38364e-01 2.98634e-01

out of 15139 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2752, 18%
LFC < 0 (down)     : 2232, 15%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4210        Min.   :      2   Length:4210        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    137   Class :character   1st Qu.:1.017e-06   1st Qu.:1.462e-05  
 Mode  :character   Median :    415   Mode  :character   Median :2.178e-04   Median :1.566e-03  
                    Mean   :   2771                      Mean   :2.052e-03   Mean   :8.622e-03  
                    3rd Qu.:   1153                      3rd Qu.:2.670e-03   3rd Qu.:1.280e-02  
                    Max.   :3262641                      Max.   :1.387e-02   Max.   :4.987e-02  
Currently proccessing: soft_filtered_htss with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 16531 10 
metadata(1): version
assays(1): counts
rownames(16531): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
              baseMean log2FoldChange     lfcSE      stat      pvalue        padj
             <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001    1.028317      0.5390259  1.632370  0.330211 7.41241e-01 8.61283e-01
FUN_000002    0.426296      0.6951703  2.121768  0.327637 7.43186e-01 8.62669e-01
FUN_000003    1.876808      0.4266906  1.204549  0.354233 7.23164e-01 8.50533e-01
FUN_000004  317.673129      0.7589270  0.145429  5.218550 1.80329e-07 3.05719e-06
FUN_000005 1828.906247     -0.0319145  0.150734 -0.211727 8.32320e-01 9.16397e-01
FUN_000006  305.037785     -0.1711777  0.123180 -1.389653 1.64634e-01 3.28924e-01

out of 15292 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2861, 19%
LFC < 0 (down)     : 2328, 15%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4441        Min.   :      1   Length:4441        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    149   Class :character   1st Qu.:1.129e-06   1st Qu.:1.552e-05  
 Mode  :character   Median :    438   Mode  :character   Median :2.269e-04   Median :1.562e-03  
                    Mean   :   2993                      Mean   :2.280e-03   Mean   :9.211e-03  
                    3rd Qu.:   1166                      3rd Qu.:3.199e-03   3rd Qu.:1.469e-02  
                    Max.   :3485388                      Max.   :1.451e-02   Max.   :4.996e-02  
Currently proccessing: soft_filtered_kallisto with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 16531 10 
metadata(1): version
assays(1): counts
rownames(16531): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001   35.8637       0.361341  0.215564  1.676262 9.36868e-02 2.17619e-01
FUN_000002   20.8053       2.888951  0.484318  5.964994 2.44644e-09 6.33108e-08
FUN_000003   13.4968       0.381483  0.448651  0.850289 3.95164e-01 5.97367e-01
FUN_000004  177.9504       0.747244  0.143893  5.193061 2.06864e-07 3.42663e-06
FUN_000005 1028.6291      -0.034806  0.150954 -0.230573 8.17646e-01 9.06687e-01
FUN_000006  181.6816      -0.183051  0.110725 -1.653202 9.82898e-02 2.25442e-01

out of 15372 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2840, 18%
LFC < 0 (down)     : 2401, 16%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr            logFC                pval              adj.pval        
 Length:4434        Min.   :      1.4   Length:4434        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    103.5   Class :character   1st Qu.:9.360e-07   1st Qu.:1.298e-05  
 Mode  :character   Median :    275.8   Mode  :character   Median :1.833e-04   Median :1.270e-03  
                    Mean   :   1779.3                      Mean   :2.079e-03   Mean   :8.384e-03  
                    3rd Qu.:    696.5                      3rd Qu.:2.644e-03   3rd Qu.:1.222e-02  
                    Max.   :2104542.5                      Max.   :1.442e-02   Max.   :4.999e-02  
Currently proccessing: soft_filtered_salmon with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 16531 10 
metadata(1): version
assays(1): counts
rownames(16531): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
-- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001  33.81217      0.3565438  0.210528  1.693566 9.03478e-02 2.04178e-01
FUN_000002  18.90942      2.8460602  0.446937  6.367916 1.91614e-10 5.28854e-09
FUN_000003   1.74844      1.2352167  0.967884  1.276203 2.01884e-01 3.71830e-01
FUN_000004 157.45127      0.7678994  0.137988  5.564971 2.62196e-08 4.71063e-07
FUN_000005 803.62963     -0.0300216  0.137203 -0.218811 8.26797e-01 9.10615e-01
FUN_000006 131.08377     -0.2013792  0.118380 -1.701125 8.89195e-02 2.01860e-01

out of 15042 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2915, 19%
LFC < 0 (down)     : 2482, 17%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr            logFC                pval              adj.pval        
 Length:4599        Min.   :      0.7   Length:4599        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:     87.0   Class :character   1st Qu.:5.240e-07   1st Qu.:6.850e-06  
 Mode  :character   Median :    232.8   Mode  :character   Median :1.662e-04   Median :1.087e-03  
                    Mean   :   1477.7                      Mean   :2.174e-03   Mean   :8.244e-03  
                    3rd Qu.:    579.6                      3rd Qu.:2.783e-03   3rd Qu.:1.214e-02  
                    Max.   :1998942.6                      Max.   :1.528e-02   Max.   :4.999e-02  
Currently proccessing: soft_filtered_strgtieh with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 16531 10 
metadata(1): version
assays(1): counts
rownames(16531): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
             baseMean log2FoldChange     lfcSE      stat      pvalue        padj
            <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001   61.68424      0.2615620  0.197708  1.322973 1.85844e-01 3.57396e-01
FUN_000002   28.89694      2.8701988  0.625310  4.590042 4.43157e-06 5.01203e-05
FUN_000003    1.70305     -0.3083270  2.289997 -0.134641 8.92896e-01 9.46094e-01
FUN_000004  301.07182      0.7965472  0.147053  5.416752 6.06915e-08 1.13030e-06
FUN_000005 1671.81633     -0.0270183  0.144814 -0.186572 8.51997e-01 9.25908e-01
FUN_000006  294.36623     -0.1733301  0.111472 -1.554926 1.19964e-01 2.60692e-01

out of 14601 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2702, 19%
LFC < 0 (down)     : 2351, 16%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4256        Min.   :      2   Length:4256        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    166   Class :character   1st Qu.:9.380e-07   1st Qu.:1.287e-05  
 Mode  :character   Median :    435   Mode  :character   Median :1.969e-04   Median :1.350e-03  
                    Mean   :   2846                      Mean   :2.163e-03   Mean   :8.646e-03  
                    3rd Qu.:   1107                      3rd Qu.:2.815e-03   3rd Qu.:1.288e-02  
                    Max.   :3270757                      Max.   :1.455e-02   Max.   :4.991e-02  
Currently proccessing: soft_filtered_strgties with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 16531 10 
metadata(1): version
assays(1): counts
rownames(16531): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
              baseMean log2FoldChange     lfcSE      stat      pvalue        padj
             <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001   62.947201      0.3299902  0.210155  1.570224 1.16363e-01 2.54849e-01
FUN_000002   35.530287      2.8926589  0.627913  4.606779 4.08954e-06 4.68381e-05
FUN_000003    0.897157     -0.7209629  2.993673 -0.240829 8.09688e-01 8.99524e-01
FUN_000004  289.747892      0.7839511  0.149653  5.238472 1.61912e-07 2.73830e-06
FUN_000005 1662.675153     -0.0387502  0.143192 -0.270616 7.86686e-01 8.86032e-01
FUN_000006  273.680295     -0.1770550  0.121315 -1.459468 1.44436e-01 2.97491e-01

out of 14912 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2771, 19%
LFC < 0 (down)     : 2359, 16%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4339        Min.   :      2   Length:4339        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    182   Class :character   1st Qu.:1.024e-06   1st Qu.:1.407e-05  
 Mode  :character   Median :    453   Mode  :character   Median :2.071e-04   Median :1.423e-03  
                    Mean   :   2926                      Mean   :2.223e-03   Mean   :8.906e-03  
                    3rd Qu.:   1135                      3rd Qu.:2.869e-03   3rd Qu.:1.314e-02  
                    Max.   :3399846                      Max.   :1.455e-02   Max.   :5.000e-02  
Currently proccessing: unfiltered_htsh with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 16612 10 
metadata(1): version
assays(1): counts
rownames(16612): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
             baseMean log2FoldChange     lfcSE       stat      pvalue        padj
            <numeric>      <numeric> <numeric>  <numeric>   <numeric>   <numeric>
FUN_000001    7.71570      0.3181713  0.644834  0.4934158 6.21719e-01 7.94706e-01
FUN_000002    4.53848      3.7774501  0.984221  3.8380113 1.24035e-04 9.70596e-04
FUN_000003    2.97278      1.2463381  1.073345  1.1611717 2.45572e-01 4.47510e-01
FUN_000004  328.92849      0.8029733  0.145086  5.5344599 3.12189e-08 6.59933e-07
FUN_000005 1790.29090     -0.0151654  0.155200 -0.0977153 9.22158e-01 9.67238e-01
FUN_000006  335.07379     -0.1686328  0.113881 -1.4807774 1.38666e-01 3.00512e-01

out of 15220 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2752, 18%
LFC < 0 (down)     : 2229, 15%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4211        Min.   :      2   Length:4211        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    137   Class :character   1st Qu.:1.022e-06   1st Qu.:1.477e-05  
 Mode  :character   Median :    415   Mode  :character   Median :2.186e-04   Median :1.580e-03  
                    Mean   :   2770                      Mean   :2.052e-03   Mean   :8.671e-03  
                    3rd Qu.:   1153                      3rd Qu.:2.674e-03   3rd Qu.:1.288e-02  
                    Max.   :3262641                      Max.   :1.383e-02   Max.   :4.999e-02  
Currently proccessing: unfiltered_htss with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 16612 10 
metadata(1): version
assays(1): counts
rownames(16612): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
              baseMean log2FoldChange     lfcSE      stat      pvalue        padj
             <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001    1.028317      0.5391401  1.634638  0.329822 7.41534e-01 8.62364e-01
FUN_000002    0.426296      0.6951695  2.126042  0.326978 7.43684e-01 8.63951e-01
FUN_000003    1.876808      0.4265735  1.205931  0.353729 7.23542e-01 8.51815e-01
FUN_000004  317.673129      0.7589285  0.145485  5.216546 1.82290e-07 3.09994e-06
FUN_000005 1828.906247     -0.0319145  0.150739 -0.211721 8.32325e-01 9.17228e-01
FUN_000006  305.037785     -0.1711848  0.123349 -1.387813 1.65194e-01 3.30929e-01

out of 15373 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2865, 19%
LFC < 0 (down)     : 2327, 15%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4438        Min.   :      1   Length:4438        Min.   :0.000e+00   Min.   :0.0000000  
 Class :character   1st Qu.:    149   Class :character   1st Qu.:1.112e-06   1st Qu.:0.0000154  
 Mode  :character   Median :    436   Mode  :character   Median :2.263e-04   Median :0.0015678  
                    Mean   :   2991                      Mean   :2.268e-03   Mean   :0.0092173  
                    3rd Qu.:   1165                      3rd Qu.:3.174e-03   3rd Qu.:0.0146589  
                    Max.   :3485388                      Max.   :1.439e-02   Max.   :0.0498517  
Currently proccessing: unfiltered_kallisto with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 16612 10 
metadata(1): version
assays(1): counts
rownames(16612): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001   35.8637      0.3613187  0.216018  1.672631 9.44000e-02 2.19977e-01
FUN_000002   20.8053      2.8888583  0.485602  5.949021 2.69751e-09 6.97069e-08
FUN_000003   13.4968      0.3814586  0.449545  0.848544 3.96135e-01 5.99740e-01
FUN_000004  177.9504      0.7472523  0.144029  5.188218 2.12316e-07 3.53166e-06
FUN_000005 1028.6291     -0.0348062  0.150975 -0.230544 8.17669e-01 9.07393e-01
FUN_000006  181.6816     -0.1830523  0.110887 -1.650805 9.87784e-02 2.27451e-01

out of 15453 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2835, 18%
LFC < 0 (down)     : 2395, 15%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr            logFC                pval              adj.pval        
 Length:4428        Min.   :      1.4   Length:4428        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    103.5   Class :character   1st Qu.:9.540e-07   1st Qu.:1.331e-05  
 Mode  :character   Median :    275.8   Mode  :character   Median :1.831e-04   Median :1.278e-03  
                    Mean   :   1779.8                      Mean   :2.066e-03   Mean   :8.387e-03  
                    3rd Qu.:    695.6                      3rd Qu.:2.613e-03   3rd Qu.:1.216e-02  
                    Max.   :2104542.5                      Max.   :1.429e-02   Max.   :4.986e-02  
Currently proccessing: unfiltered_salmon with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 16612 10 
metadata(1): version
assays(1): counts
rownames(16612): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001  33.81235      0.3554540  0.225872  1.573699 1.15557e-01 2.63744e-01
FUN_000002  18.90947      2.8409739  0.500820  5.672644 1.40610e-08 3.21215e-07
FUN_000003   1.74846      1.2563643  1.131258  1.110591 2.66745e-01 4.76717e-01
FUN_000004 157.45178      0.7684704  0.152846  5.027748 4.96272e-07 7.71339e-06
FUN_000005 803.63155     -0.0302264  0.147730 -0.204606 8.37880e-01 9.25721e-01
FUN_000006 131.08428     -0.2023537  0.131968 -1.533355 1.25189e-01 2.80353e-01

out of 15123 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2661, 18%
LFC < 0 (down)     : 2217, 15%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr            logFC                pval              adj.pval        
 Length:4138        Min.   :      1.2   Length:4138        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:     90.2   Class :character   1st Qu.:7.900e-07   1st Qu.:1.154e-05  
 Mode  :character   Median :    238.1   Mode  :character   Median :1.977e-04   Median :1.444e-03  
                    Mean   :   1605.7                      Mean   :2.129e-03   Mean   :9.081e-03  
                    3rd Qu.:    614.6                      3rd Qu.:2.809e-03   3rd Qu.:1.369e-02  
                    Max.   :1998955.1                      Max.   :1.366e-02   Max.   :4.993e-02  
Currently proccessing: unfiltered_strgtieh with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 16612 10 
metadata(1): version
assays(1): counts
rownames(16612): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
             baseMean log2FoldChange     lfcSE      stat      pvalue        padj
            <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001   61.68424      0.2615580  0.197798  1.322349 1.86052e-01 3.59328e-01
FUN_000002   28.89694      2.8702001  0.625243  4.590534 4.42114e-06 5.02408e-05
FUN_000003    1.70305     -0.3083201  2.289714 -0.134654 8.92885e-01 9.46454e-01
FUN_000004  301.07182      0.7965483  0.147091  5.415337 6.11733e-08 1.14413e-06
FUN_000005 1671.81633     -0.0270184  0.144836 -0.186545 8.52017e-01 9.26700e-01
FUN_000006  294.36623     -0.1733332  0.111531 -1.554130 1.20153e-01 2.62280e-01

out of 14682 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2702, 18%
LFC < 0 (down)     : 2344, 16%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4256        Min.   :      2   Length:4256        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    165   Class :character   1st Qu.:9.310e-07   1st Qu.:1.284e-05  
 Mode  :character   Median :    433   Mode  :character   Median :1.977e-04   Median :1.363e-03  
                    Mean   :   2845                      Mean   :2.158e-03   Mean   :8.673e-03  
                    3rd Qu.:   1106                      3rd Qu.:2.819e-03   3rd Qu.:1.297e-02  
                    Max.   :3270757                      Max.   :1.448e-02   Max.   :4.996e-02  
Currently proccessing: unfiltered_strgties with DESeq 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Warning in DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors
class: DESeqDataSet 
dim: 16612 10 
metadata(1): version
assays(1): counts
rownames(16612): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat

Results Below: 
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
log2 fold change (MLE): Treat Restricted vs AdLib 
Wald test p-value: Treat Restricted vs AdLib 
DataFrame with 6 rows and 6 columns
              baseMean log2FoldChange     lfcSE      stat      pvalue        padj
             <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
FUN_000001   62.947201      0.3299895  0.210220  1.569731 1.16478e-01 2.56088e-01
FUN_000002   35.530287      2.8926593  0.627866  4.607125 4.08274e-06 4.70143e-05
FUN_000003    0.897157     -0.7209628  2.993668 -0.240829 8.09687e-01 9.00118e-01
FUN_000004  289.747892      0.7839520  0.149683  5.237412 1.62844e-07 2.76816e-06
FUN_000005 1662.675153     -0.0387503  0.143209 -0.270585 7.86710e-01 8.86720e-01
FUN_000006  273.680295     -0.1770577  0.121357 -1.458988 1.44568e-01 2.98926e-01

out of 14993 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2769, 18%
LFC < 0 (down)     : 2356, 16%
outliers [1]       : 0, 0%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

NULL
    GeneID             meanExpr          logFC                pval              adj.pval        
 Length:4332        Min.   :      2   Length:4332        Min.   :0.000e+00   Min.   :0.000e+00  
 Class :character   1st Qu.:    181   Class :character   1st Qu.:1.015e-06   1st Qu.:1.404e-05  
 Mode  :character   Median :    453   Mode  :character   Median :2.047e-04   Median :1.417e-03  
                    Mean   :   2930                      Mean   :2.197e-03   Mean   :8.865e-03  
                    3rd Qu.:   1136                      3rd Qu.:2.849e-03   3rd Qu.:1.314e-02  
                    Max.   :3399846                      Max.   :1.444e-02   Max.   :4.996e-02  
Currently proccessing: hard_filtered_htsh with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2001 

Currently proccessing: hard_filtered_htss with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  1996 

Currently proccessing: hard_filtered_kallisto with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2155 

Currently proccessing: hard_filtered_salmon with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2116 

Currently proccessing: hard_filtered_strgtieh with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  1904 

Currently proccessing: hard_filtered_strgties with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  1936 

Currently proccessing: pipeline_filtered_htsh with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2101 

Currently proccessing: pipeline_filtered_htss with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2157 

Currently proccessing: pipeline_filtered_kallisto with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2316 

Currently proccessing: pipeline_filtered_salmon with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2225 

Currently proccessing: pipeline_filtered_strgtieh with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  1982 

Currently proccessing: pipeline_filtered_strgties with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2052 

Currently proccessing: soft_filtered_htsh with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2033 

Currently proccessing: soft_filtered_htss with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2100 

Currently proccessing: soft_filtered_kallisto with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2274 

Currently proccessing: soft_filtered_salmon with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2165 

Currently proccessing: soft_filtered_strgtieh with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  1933 

Currently proccessing: soft_filtered_strgties with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2023 

Currently proccessing: unfiltered_htsh with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2030 

Currently proccessing: unfiltered_htss with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2103 

Currently proccessing: unfiltered_kallisto with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2277 

Currently proccessing: unfiltered_salmon with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2168 

Currently proccessing: unfiltered_strgtieh with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  1932 

Currently proccessing: unfiltered_strgties with edgeR 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
The number of significant DE genes is:  2028 

Currently proccessing: hard_filtered_htsh with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01:  1970 
Results where FDR is less than 0.05:  4043 
Results where FDR is less than 0.1:  5169 
       groupAdLib - groupRestricted
Down                           2066
NotSig                        10258
Up                             1977
Currently proccessing: hard_filtered_htss with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  2067 
Results where FDR is less than 0.05:  4140 
Results where FDR is less than 0.1:  6661 
       groupAdLib - groupRestricted
Down                           2092
NotSig                        10161
Up                             2048
Currently proccessing: hard_filtered_kallisto with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  2308 
Results where FDR is less than 0.05:  4315 
Results where FDR is less than 0.1:  6755 
       groupAdLib - groupRestricted
Down                           2172
NotSig                         9986
Up                             2143
Currently proccessing: hard_filtered_salmon with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  2046 
Results where FDR is less than 0.05:  4042 
Results where FDR is less than 0.1:  5143 
       groupAdLib - groupRestricted
Down                           2068
NotSig                        10259
Up                             1974
Currently proccessing: hard_filtered_strgtieh with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  1806 
Results where FDR is less than 0.05:  3968 
Results where FDR is less than 0.1:  5223 
       groupAdLib - groupRestricted
Down                           1989
NotSig                        10333
Up                             1979
Currently proccessing: hard_filtered_strgties with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  1816 
Results where FDR is less than 0.05:  4018 
Results where FDR is less than 0.1:  5235 
       groupAdLib - groupRestricted
Down                           2014
NotSig                        10283
Up                             2004
Currently proccessing: pipeline_filtered_htsh with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  2081 
Results where FDR is less than 0.05:  4033 
Results where FDR is less than 0.1:  5116 
       groupAdLib - groupRestricted
Down                           2066
NotSig                         9945
Up                             1967
Currently proccessing: pipeline_filtered_htss with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  2100 
Results where FDR is less than 0.05:  4150 
Results where FDR is less than 0.1:  5262 
       groupAdLib - groupRestricted
Down                           2138
NotSig                        10181
Up                             2012
Currently proccessing: pipeline_filtered_kallisto with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  2313 
Results where FDR is less than 0.05:  4320 
Results where FDR is less than 0.1:  5501 
       groupAdLib - groupRestricted
Down                           2208
NotSig                        10114
Up                             2112
Currently proccessing: pipeline_filtered_salmon with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  2123 
Results where FDR is less than 0.05:  4015 
Results where FDR is less than 0.1:  5141 
       groupAdLib - groupRestricted
Down                           2059
NotSig                         9653
Up                             1956
Currently proccessing: pipeline_filtered_strgtieh with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  1994 
Results where FDR is less than 0.05:  4031 
Results where FDR is less than 0.1:  5251 
       groupAdLib - groupRestricted
Down                           2023
NotSig                         9550
Up                             2008
Currently proccessing: pipeline_filtered_strgties with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  1937 
Results where FDR is less than 0.05:  4085 
Results where FDR is less than 0.1:  5292 
       groupAdLib - groupRestricted
Down                           2042
NotSig                         9917
Up                             2043
Currently proccessing: soft_filtered_htsh with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  1996 
Results where FDR is less than 0.05:  4112 
Results where FDR is less than 0.1:  7160 
       groupAdLib - groupRestricted
Down                           2103
NotSig                        12419
Up                             2009
Currently proccessing: soft_filtered_htss with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  2039 
Results where FDR is less than 0.05:  4211 
Results where FDR is less than 0.1:  7075 
       groupAdLib - groupRestricted
Down                           2171
NotSig                        12320
Up                             2040
Currently proccessing: soft_filtered_kallisto with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  2259 
Results where FDR is less than 0.05:  4373 
Results where FDR is less than 0.1:  7162 
       groupAdLib - groupRestricted
Down                           2249
NotSig                        12158
Up                             2124
Currently proccessing: soft_filtered_salmon with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  2069 
Results where FDR is less than 0.05:  4091 
Results where FDR is less than 0.1:  7264 
       groupAdLib - groupRestricted
Down                           2095
NotSig                        12440
Up                             1996
Currently proccessing: soft_filtered_strgtieh with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  1823 
Results where FDR is less than 0.05:  4005 
Results where FDR is less than 0.1:  7831 
       groupAdLib - groupRestricted
Down                           1990
NotSig                        12526
Up                             2015
Currently proccessing: soft_filtered_strgties with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  1864 
Results where FDR is less than 0.05:  4099 
Results where FDR is less than 0.1:  7485 
       groupAdLib - groupRestricted
Down                           2053
NotSig                        12432
Up                             2046
Currently proccessing: unfiltered_htsh with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  1986 
Results where FDR is less than 0.05:  4092 
Results where FDR is less than 0.1:  7135 
       groupAdLib - groupRestricted
Down                           2119
NotSig                        12520
Up                             1973
Currently proccessing: unfiltered_htss with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  2038 
Results where FDR is less than 0.05:  4211 
Results where FDR is less than 0.1:  7081 
       groupAdLib - groupRestricted
Down                           2171
NotSig                        12401
Up                             2040
Currently proccessing: unfiltered_kallisto with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  2256 
Results where FDR is less than 0.05:  4368 
Results where FDR is less than 0.1:  7165 
       groupAdLib - groupRestricted
Down                           2245
NotSig                        12244
Up                             2123
Currently proccessing: unfiltered_salmon with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  2065 
Results where FDR is less than 0.05:  4087 
Results where FDR is less than 0.1:  7266 
       groupAdLib - groupRestricted
Down                           2091
NotSig                        12525
Up                             1996
Currently proccessing: unfiltered_strgtieh with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  1832 
Results where FDR is less than 0.05:  4004 
Results where FDR is less than 0.1:  7833 
       groupAdLib - groupRestricted
Down                           1990
NotSig                        12608
Up                             2014
Currently proccessing: unfiltered_strgties with Limma-Voom 

Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 

Results where FDR is less than 0.01:  1869 
Results where FDR is less than 0.05:  4092 
Results where FDR is less than 0.1:  7485 
       groupAdLib - groupRestricted
Down                           2051
NotSig                        12520
Up                             2041

It would be great to hold all plots for each data set until the end (exploratory and mean difference plots)

---
title: "DGE Analyses"
output: 
  html_notebook:
   code_folding: show
author: "Amanda D. Clark"
---

## Purpose
WORDS   
### Sources & Resources
Sources and resources are linked where applicable
## Setting Up
### Setting Up Environment
```{r}
# clear workspace
rm(list=ls(all.names=TRUE))
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

#BiocManager::install("ballgown")
#BiocManager::install("RNAseq123")
library(devtools)
library(tidyverse)
library(readr) #readr is faster, but makes tibbles...not sure how well tibbles work with programs expecting matrices. row names can't be specified with readr
library(DESeq2)
library(SummarizedExperiment)
library(edgeR)
library(limma)
library(tools)
library(RNAseq123)
library(Glimma)
library(ballgown)
library(genefilter)
library(RColorBrewer)
library(cowplot)




# directory for input files
indir <- "../R_outputs/QuantPrep_Filter"

# make a directory for output files
if (! dir.exists("../R_outputs/DGE_Analyses")) {
 dir.create("../R_outputs/DGE_Analyses")
}
outdir <- "../R_outputs/DGE_Analyses"
```


### Setting up Input Files

words

I can individual load the data for each pipeline separately, and add them to a list 
```{r}
# reading in count data
hf_htsh <- read.csv(file.path(indir, "hard_filtered_htsh.csv"), header = T, row.names = 1 ) #Hard-filtered (see Purpose) count matrices
hf_htss <- read.csv(file.path(indir,"hard_filtered_htss.csv"), header = T, row.names = 1 )
hf_kall <- read.csv(file.path(indir,"hard_filtered_kallisto.csv"), header = T, row.names = 1 )
hf_salm <- read.csv(file.path(indir,"hard_filtered_salmon.csv"), header = T, row.names = 1 )
hf_strh <- read.csv(file.path(indir,"hard_filtered_strgtieh.csv"), header = T, row.names = 1 )
hf_strs <- read.csv(file.path(indir,"hard_filtered_strgties.csv"), header = T, row.names = 1)

sf_htsh <- read.csv(file.path(indir,"soft_filtered_htsh.csv"), header = T, row.names = 1)  #Soft-filtered count matrices
sf_htss <- read.csv(file.path(indir,"soft_filtered_htss.csv"), header = T, row.names = 1)
sf_kall <- read.csv(file.path(indir,"soft_filtered_kallisto.csv"), header = T, row.names = 1)
sf_salm <- read.csv(file.path(indir,"soft_filtered_salmon.csv"), header = T, row.names = 1)
sf_strh <- read.csv(file.path(indir,"soft_filtered_strgtieh.csv"), header = T, row.names = 1)
sf_strs <- read.csv(file.path(indir,"soft_filtered_strgties.csv"), header = T, row.names = 1)

# make list of dataframes
datlist <- list(hf_htsh=hf_htsh,hf_htss=hf_htss,hf_kall=hf_kall,hf_salm=hf_salm,
                hf_strh=hf_strh,hf_strs=hf_strs,sf_htsh=sf_htsh,sf_htss=sf_htss,
                sf_kall=sf_kall,sf_salm=sf_salm,sf_strh=sf_strh,sf_strs=sf_strs)

# adding in sample metadata
samples <- read.table("../R_inputs/samples.txt", header = T) #Read in sample table
  #make new column for treatment (control vs Experiment)
samples <- samples %>% dplyr::select(SRRID, SAMPNAME) %>%  #select the sample ID and name
  mutate(Treat = ifelse(grepl("C", samples$SAMPNAME), "Restricted", #make new column for treatment (restricted vs adlib)
                            ifelse(grepl("E", samples$SAMPNAME), "AdLib", "Error")))
## make a note to change 
head(samples)

```

Or I can generate a List object with the file names and data
```{r}
# sample metadata
samples <- read.table("../R_inputs/samples.txt", header = T) #Read in sample table
  #make new column for treatment (control vs Experiment)
samples <- samples %>% dplyr::select(SRRID, SAMPNAME) %>%  #select the sample ID and name
  mutate(Treat = ifelse(grepl("C", samples$SAMPNAME), "Restricted", #make new column for treatment (restricted vs adlib)
                            ifelse(grepl("E", samples$SAMPNAME), "AdLib", "Error")))

# generate data vector
files <- list() # empty list for file paths
count_data <- vector(mode = "list", length = 2) # empty list for data vector
files <- list.files(indir, ".csv", full.names = T) # populate list from input directory

count_data <- list(f_name = c(file_path_sans_ext(basename(files))), f_content = files %>% map(read.csv, header = T, row.names =1)) # populate list with file names, paths, and content
names(count_data$f_content) <- count_data$f_name # name matrices based on file names
```



## DGE Functions 

### Library Visualization Function

words
https://multithreaded.stitchfix.com/blog/2015/10/15/multiple-hypothesis-testing/ # explains MHT well, and in depth. pull what is needed, link the rest

```{r}
run.Vis <- function(x, y) {
  y <- y[[1]][1]
  cat("Currently visualizing libraries for pipeline:", y, "\n\n") #print which file is being processed
  colnames(x) <- c(samples$SAMPNAME) #add column names
  cat("\nColumn names:", names(x), "\n\n") #print column names
  # possible issue, this object may not be the same across programs, but that shouldn't be a problem if it's just a data format. all information going into downstream programs are the same.
  dat <- DGEList(x, group = as.factor(c(samples$Treat))) #create DGEList object, merging counts, metadata, and specifies the grouping variable for samples 
  print(dat)
  
  
  # look at raw lib size in parallel
  barplot(dat$samples$lib.size*1e-6, names = 1:10, ylab = "Library size (millions)", xlab = y) #Make barplot of the library sizes
  # Saved in the object
  b.plot = recordPlot()
  dev.off()
  
  cpm <- cpm(dat)
  lcpm <- cpm(dat, log = TRUE)
  L <- mean(dat$samples$lib.size) * 1e-6
  M <- median(dat$samples$lib.size) * 1e-6
  cat("\n", "Mean and Median Library Size in Millions:", c(L, M),"\n")
  cnttab <- table(rowSums(dat$counts == 0) == 10) 
  cnttab 
 
  # MDS Plots
  col.group <- as.factor(c(samples$Treat))
  levels(col.group) <- brewer.pal(nlevels(col.group), "Set1")
  col.group <- as.character(col.group)
  mds.plot <- plotMDS(lcpm, labels = samples$SAMPNAME, col = col.group, main = paste0("Pipeline: ", y))
  # Saved in the object
  mds.plot = recordPlot()
  dev.off()
  # Density Plots to observe filtering effects (may need to reformat output to a list and then organize them after running the function to compare hard/soft filters)
  col.group <- brewer.pal(ncol(x), "Set3")
  lcpm.cutoff <- log2(10/M + 2/L)
  plot(density(lcpm[,1]), col = col.group[1], lwd = 2, ylim = c(0,0.26), las = 2, main = y)
  for (i in 2:ncol(x)){
  den <- density(lcpm[,i])
  lines(den$x, den$y, col = col.group[i], lwd = 2)
  }
  legend("topright", samples$SAMPNAME, text.col = col.group, bty = "n")
  abline(v = lcpm.cutoff, lty = 3)
  # Saved in the object
  density.res = recordPlot()
  dev.off()
 
  # Box Plots
  dat2 <- calcNormFactors(dat, method = "TMM")
  lcpm2 <- cpm(dat2, log=TRUE)
  print(head(lcpm))
  print(head(lcpm2))
  dat$samples$norm.factors
  dat2$samples$norm.factors
  par(mfrow=c(1,2)) #getting 
  boxplot(lcpm, las = 2, col = col.group, main = "Unnormalized data", ylab = "Log-CPM")
  boxplot(lcpm2, las = 2, col = col.group, main = "Normalized data", ylab = "Log-CPM")
  box.res = recordPlot()
  dev.off()
  
  pdf(file = paste0(outdir,"/",y,"_dataExploration.pdf"))
  print(b.plot)
  print(mds.plot)
  print(density.res)
  print(box.res)
  dev.off()
}
```

### DESeq2 DGE Function

words

```{r}
run.DESeq <- function(x, y) {
  y <- y[[1]][1]
  cat("Currently proccessing:", y, "with DESeq \n") #print which file is being processed
  colnames(x) <- c(samples$SAMPNAME) #add column names
  cat("\nColumn names:", names(x), "\n\n") #print column names 
  
  dat <- DESeqDataSetFromMatrix(countData = x, colData = samples, 
                                design = ~Treat) #create DESeq object, merging counts, metadata, and specifies the predictor variable for gene counts
  print(dat)
  cat("\nResults Below: \n")
  mod <- DESeq(dat, minReplicatesForReplace = Inf) #running the DESeq function. minReplicatesForReplace=Inf prevents replacement of outlier counts
  res <- results(mod, independentFiltering = FALSE,cooksCutoff = FALSE, contrast = c("Treat", "Restricted", "AdLib"),
                 pAdjustMethod = "fdr") #store results table. skipping outlier adjustments and additional low count filtering. using a false discovery rate p-value adjustment
  print(head(res))
  print(summary(res))
  
  # make data frame output, reorder, and filter
  reslist <- list( GeneID = res@rownames, meanExpr = res@listData$baseMean, logFC = res@listData$log2FoldChange, 
                   pval = res@listData$pvalue, adj.pval = res@listData$padj)
  resdf <- as.data.frame(do.call(cbind, reslist)) %>% mutate(meanExpr = as.numeric(meanExpr), pval = as.numeric(pval), 
                                                             adj.pval = as.numeric(adj.pval))
  
  resOrdered <- resdf[order(as.numeric(resdf$adj.pval)),] #results reordered by the adjusted pvalue
  resSig <- subset(resOrdered, as.numeric(adj.pval) < 0.05)
  print(head (resSig))
  print(summary(resSig))
  
  out <- resSig
  
  write.csv(as.data.frame(out),file=paste0(outdir,"/",y,"_DESeq2.csv")) #write results to a new csv
  
  pdf(file = paste0(outdir,"/",y,"_DESeq.pdf"))
  DESeq2::plotMA(res)
  dev.off()
  
}
```


### edgeR DGE Function

words

```{r}
run.EdgeR <- function(x, y) {
  y <- y[[1]][1]
  cat("Currently proccessing:", y, "with edgeR \n") #print which file is being processed
  colnames(x) <- c(samples$SAMPNAME) #add column names
  cat("\nColumn names:", names(x), "\n\n") #print column names 
  
  
  dat <- DGEList(x, group = samples$Treat) #create DGEList object, merging counts, metadata, and specifies the grouping variable for samples 
  
  # est common & tagwise dispersion
  mod <- estimateCommonDisp(dat)
  mod <- estimateTagwiseDisp(mod)
  
  # perform exact test btwn caloric restriction & ad lib groups, store as 'res'
  modTest <- exactTest(mod)
  res <- topTags(modTest, n = nrow(modTest$table))
  
  # extract significant differentially expressed genes, sort, & write to csv
  resOrdered <- res$table[order(res$table$logFC),]
  resSig <- resOrdered[resOrdered$FDR<0.05,]
  print(head(resOrdered))
  
  out <- resSig %>% dplyr::select(logFC, logCPM, PValue, FDR) %>% dplyr::rename(meanExpr = logCPM, pval = PValue, adj.pval = FDR)
  write.csv(as.data.frame(out),file = paste0(outdir,"/",y,"_edgeR.csv")) #write results to a new csv

  cat("The number of significant DE genes is: ", nrow(resSig),"\n\n")
  
  pdf(file = paste0(outdir,"/",y,"_edgeR.pdf"))
  edgeR::plotMD.DGEExact(modTest)
  #plotMD(res, column = 5, main = paste(colnames(res)[1],y,sep = "_"), xlim = c(-0.1,20))
  dev.off()
  
  
}
```

### LimmaVoom DGE Function

words
Summarize & simplify:
"What is voom doing?
Counts are transformed to log2 counts per million reads (CPM), where “per million reads” is defined based on the normalization factors we calculated earlier
A linear model is fitted to the log2 CPM for each gene, and the residuals are calculated
A smoothed curve is fitted to the sqrt(residual standard deviation) by average expression (see red line in plot above)
The smoothed curve is used to obtain weights for each gene and sample that are passed into limma along with the log2 CPMs.
More details at https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29"



```{r}
run.LimVoo <- function(x,y) {
  y <- y[[1]][1]
  cat("Currently proccessing:", y ,"with Limma-Voom \n") #print which file is being processed by which function as a sanity check
  colnames(x) <- c(samples$SAMPNAME) #add column names to the data object
  cat("\nColumn names:", names(x), "\n\n") #print column names as a sanity check for order
  
  Treat <- c(samples$Treat)
  group=Treat
  dat <- DGEList(x, group = Treat) #create DGEList object, merging counts, metadata, and specifies the grouping variable for samples 
  #print(class(dat))
  
  # Normalization (based on the plots, is this necessary?)
  dat <- calcNormFactors(dat, method = "TMM")
  dat$samples$norm.factors
  dat
  
  mod <- model.matrix(~0 + group)
  mod
  
  varMod <- voom(dat, mod, plot = T) # Would be nice to stop and compare hard/soft filtering again here
  
  modFit <- lmFit(varMod, mod)
  #print(head(coef(modFit)))
  
  contr <- makeContrasts(groupAdLib - groupRestricted, levels = colnames(coef(modFit)))
  #print(head(contr))
  
  fitContr <- contrasts.fit(modFit, contr)
  fitContr <- eBayes(fitContr)
  
  res <- topTable(fitContr, sort.by = "P", n = Inf)
  print(head(res, 8)) 
  cat("Results where FDR is less than 0.01: ", length(which(res$adj.P.Val < 0.01)), "\n")
  cat("Results where FDR is less than 0.05: ", length(which(res$adj.P.Val < 0.05)), "\n")
  cat("Results where FDR is less than 0.1: ", length(which(res$adj.P.Val < 0.1)), "\n")
  
  out <- res %>% dplyr::select(logFC, AveExpr, P.Value, adj.P.Val) %>% dplyr::rename(meanExpr = AveExpr, pval = P.Value, adj.pval = adj.P.Val)
  print(head(out))
  
  etRes <- decideTests(fitContr)
  print(summary(etRes))
  
  write.csv(as.data.frame(out),file = paste0(outdir,"/",y,"_LimmaVoom.csv")) #write results to a new csv
  
  pdf(file = paste0(outdir,"/",y,"_LimmaVoom.pdf"))
  plotMD(fitContr, column = 1, status = etRes[,1], main = paste(colnames(fitContr)[1],y,sep = "_"), xlim = c(-0.1,20))
  varMod <- voom(dat, mod, plot = T)
  dev.off()
  
}
```

## Ballgown DGE 
words
Summarize & simplify:
There are many ballgown specific input files that make it difficult to use the previously filtered data with these programs. Only things processed with stringtie with the for ballgown output are readily formatted for this program. These will not be run within a function.?
Some resources: https://rnabio.org/module-03-expression/0003/04/01/DE_Visualization/ 
https://rstudio-pubs-static.s3.amazonaws.com/289617_cb95459057764fdfb4c42b53c69c6d3f.html
https://davetang.org/muse/2017/10/25/getting-started-hisat-stringtie-ballgown/
```{r}

# We loaded our "phenotype" data in the beginning, so we don't need to repeat this step. 
# create a ballgown object for the star and hisat2 outputs; stringtie and ballgown are complementary programs

bg_star <- ballgown(dataDir = "../R_inputs/ballgown_star/", samplePattern = "SRR", pData = samples)
bg_hisat <- ballgown(dataDir = "../R_inputs/ballgown_hisat/", samplePattern = "SRR", pData = samples)

# check out the objects
class(bg_star)
class(bg_hisat)

bg_star
bg_hisat

# filtering, following previous logic for pipeline specific

bg_star_f1 <- ballgown::subset(bg_star, 
                               "rowSums(gexpr(bg_star)==0) <= 5", 
                               genomesubset=TRUE) # first filter, remove rows with 6 or more 0s
bg_star_f1
bg_star_fltrd <- ballgown::subset(bg_star_f1, 
                                  "rowSums(gexpr(bg_star_f1)) >= 21") # second filter, remove rows that sum to less than 21
bg_star_fltrd


bg_hisat_f1 <- ballgown::subset(bg_hisat, 
                                "rowSums(gexpr(bg_hisat)==0) <= 5", 
                                genomesubset=TRUE) # first filter, remove rows with 6 or more 0s
bg_hisat_f1
bg_hisat_fltrd <- ballgown::subset(bg_hisat_f1, 
                                   "rowSums(gexpr(bg_hisat_f1)) >= 21") # second filter, remove rows that sum to less than 21
bg_hisat_fltrd

# run dge analysis and output data to file (should filter by qvalue? -- what did I filter by with other tables?)
bg_star_genes <- stattest(bg_star_fltrd,
                          feature="gene",
                          covariate="Treat",
                          getFC=TRUE, meas="FPKM")
dim(bg_star_genes)
table(bg_star_genes$qval<0.05)


bg_hisat_genes <- stattest(bg_hisat_fltrd,
                          feature="gene",
                          covariate="Treat",
                          getFC=TRUE, meas="FPKM")

dim(bg_hisat_genes)
table(bg_hisat_genes$qval<0.05)

# output results
# extract significant differentially expressed genes, sort, & write to csv

bg_hisat_genes[,"de"] <- log2(bg_hisat_genes[,"fc"])
sigpi = which(bg_hisat_genes[,"pval"]<0.05)
sigp = bg_hisat_genes[sigpi,]
sigde = which(abs(sigp[,"de"]) >= 2)
sig_tn_de = sigp[sigde,]
o = order(sig_tn_de[,"qval"], -abs(sig_tn_de[,"de"]), decreasing=FALSE)
output = sig_tn_de[o,c("id","fc","pval","qval","de")]
write.csv(as.data.frame(output),file = paste0(outdir,"/","hisat_Ballgown.csv")) #write results to a new csv

bg_star_genes[,"de"] <- log2(bg_star_genes[,"fc"])
sigpi = which(bg_hisat_genes[,"pval"]<0.05)
sigp = bg_hisat_genes[sigpi,]
sigde = which(abs(sigp[,"de"]) >= 2)
sig_tn_de = sigp[sigde,]
o = order(sig_tn_de[,"qval"], -abs(sig_tn_de[,"de"]), decreasing=FALSE)
output <- sig_tn_de[o,c("id","fc","pval","qval","de")]
write.csv(as.data.frame(output),file = paste0(outdir,"/","star_Ballgown.csv")) #write results to a new csv

# visualize results

bg_star_genes$mean <- rowMeans(texpr(bg_star_fltrd))
bg_star_plot <- ggplot(bg_star_genes, aes(log2(mean), log2(fc), colour = qval<0.05)) +
  scale_color_manual(values=c("#999999", "#FF0000")) +
  geom_point() +
  geom_hline(yintercept=0)

bg_hisat_genes$mean <- rowMeans(texpr(bg_hisat_fltrd))
bg_hisat_plot <- ggplot(bg_hisat_genes, aes(log2(mean), log2(fc), colour = qval<0.05)) +
  scale_color_manual(values=c("#999999", "#FF0000")) +
  geom_point() +
  geom_hline(yintercept=0)

bg_star_plot
bg_hisat_plot

```




## Apply DGE functions

I am applying each function in loop here
```{r}

cnt <- 1
for (i in datlist){
run.Vis(i, names(datlist)[cnt])
cnt <- cnt +1
}

cnt <- 1
for (i in datlist){
run.DESeq(i, names(datlist)[cnt])
cnt <- cnt +1
}

cnt <- 1
for (i in datlist){
run.EdgeR(i, names(datlist)[cnt])
cnt <- cnt +1
}

cnt <- 1
for (i in datlist){
run.LimVoo(i, names(datlist)[cnt])
cnt <- cnt +1
}
```

I can also map or mapply over the data set, looping over each DEseq function
```{r}
# List of functions needed to run on count matrices
funct <- c( "run.Vis","run.DESeq", "run.EdgeR", "run.LimVoo")

# Apply each function to each count data set in List object
for (func in funct) {
mapply(func, count_data$f_content, count_data$f_name)
}

```
It would be great to hold all plots for each data set until the end (exploratory and mean difference plots)

